A New Visual Adaptive Scoring System for Sleep Recordings

Transcription

1 SARI-LEENA HIMANEN A New Visual Adaptive Scoring System for Sleep Recordings Development and Application to the Multiple Sleep Latency Test University of Tampere Tampere 2000

2 A New Visual Adaptive Scoring System for Sleep Recordings Acta Universitatis Tamperensis 769

3 ACADEMIC DISSERTATION University of Tampere, Medical School Tampere University Hospital, Department of Clinical Neurophysiology Finland Supervised by Docent Joel Hasan University of Tampere Reviewed by Docent Ilkka Lehtinen University of Turku Docent Uolevi Tolonen University of Oulu Distribution University of Tampere Sales Office P.O. Box Tampere Finland Tel Fax taju@uta.fi Cover design by Juha Siro Printed dissertation Acta Universitatis Tamperensis 769 ISBN ISSN Electronic dissertation Acta Electronica Universitatis Tamperensis 61 ISBN ISSN X Tampereen yliopistopaino Oy Juvenes Print Tampere 2000

4 SARI-LEENA HIMANEN A New Visual Adaptive Scoring System for Sleep Recordings Development and Application to the Multiple Sleep Latency Test ACADEMIC DISSERTATION To be presented, with the permission of the Faculty of Medicine of the University of Tampere, for public discussion in the small auditorium of Building B, Medical School of the University of Tampere, Medisiinarinkatu 3, Tampere, on October 27th, 2000, at 12 o clock. University of Tampere Tampere 2000

5 CONTENTS ABBREVIATIONS 9 1. INTRODUCTION REVIEW OF THE LITERATURE Evolution of sleep EEG description Early days of sleep staging Further development of the sleep process Neurophysiological basis of NREM sleep and NREM sleep EEG Slow oscillation Impact of slow oscillation on cortical EEG Other periodicities in sleep Cyclic Alternating Pattern Standardised scoring system Rules of the standardised scoring system Evaluation of the standardised sleep stage scoring system 19 Scoring with epochs ignores short lasting oscillations 19 Topographical aspects 19 RKS does not take into account the continuity of the sleep process and phasic events 20 RKS is not sufficient to describe drowsy states 21 Atypical PSG patterns 23 Summary of the criticism of RKS Visual methods to supplement and improve RKS Computerized sleep analysis Sleep stage scoring Spectral analysis FFT in sleep-wake transition 27 Topographical studies by FFT Adaptive segmentation Automatic adaptive segmentation Visual adaptive segmentation Multiple sleep latency test in vigilance study Definition of MSLT Problem of sleep onset 32 Moment of sleep onset by EEG, subjective assessment, reaction times and hypnagogic imageries 32 SEMs at sleep onset 33 Summarising the definition of sleep onset Value of MSLT in determining sleepiness Positive findings Negative findings MSLT and nocturnal polygraphic parameters 35 5

6 MSLT in sleep apnea syndrome Evaluation of MSLT as an indicator of sleepiness Increasing the sensitivity of MSLTs PURPOSE OF THE STUDY SUBJECTS AND METHODS Subjects Recordings Psychometric tests Visual scoring Night recordings MSLT scoring 45 Repeatability of VASS 47 Definition of MSLT latencies Spectral analysis Calculation of EEG spectra Comparison between RKS and VASS in the differentiation of wakefulness and light sleep by alpha and delta-theta bands Peak frequencies Frequency band power characteristics of VASS stages Statistical methods Ethical considerations RESULTS Sleep parameters of night recordings Sleep in MSLT naps: comparison between patient and control groups General characteristics Sleep parameters in MSLT recordings MSLT latencies Characteristics of VASS parameters with comparison to RKS MSLT parameters Latencies Clinical evaluation of MSLT Effects of VASS on sleep analysis Effects of VASS on epoch lengths Stage transitions in VASS Effects of VASS on hypnograms 67 6

7 5.6. RKS / VASS agreement Periodicities at sleep onset Psychometric tests Comparison between patient and control groups Correlation coefficient analysis Stepwise binary logistic regression analysis Repeatability of VASS Spectral database Spectral comparison between RKS and VASS Differentiation between wakefulness and S1 by alpha and delta-theta power bands Topographical differences in alpha and delta-theta power bands Peak frequencies of RKS and VASS stages Summary of differences between RKS and VASS Frequency band power characteristics of VASS stages Peak frequency topography in VASS Topographical band power differences within stages Band power differences between stages Band power differences between adjacent VASS stages WA versus SA WAF versus SAF WL versus DL S1VASS versus S2VASS Aa2 versus S2VASS DISCUSSION Temporal resolution Hypnograms Quantitative parameters reflecting increased temporal resolution MSLT and nocturnal parameters, psychometric tests and subjective assessment Nocturnal parameters Effects of VASS in clinical evaluation of the MSLT Psychometric tests Benefits of VASS on the study of sleep dynamics Spectral comparison between RKS and VASS Alpha and delta-theta power Differences in peak frequencies between RKS and VASS VASS as a basis for automatic analysis 109 7

8 6.5. Frequency band power characteristics of VASS stages Topographical differences of the peak frequencies in VASS Band power differences between adjacent VASS stages 110 WA versus SA 110 WAF versus SAF 111 WL versus DL 111 S1VASS versus S2VASS 112 Aa2 versus S2VASS and VASS alpha stages 112 EMGW and MTW Band power characteristics along with VASS stages 112 Alpha power 112 Theta power 113 Delta power 114 Sigma power 115 Beta power VALIDITY OF STAGE DIVISION BY MORPHOLOGY FFT AS QUALITY CONTROL OF VISUAL SCORING VASS AS A SCORING SYSTEM Repeatability of VASS VASS in clinical practice Disadvantages of VASS Advantages of VASS Position of VASS in sleep studies CONCLUSIONS SUMMARY 123 ACKNOWLEDGEMENTS 125 REFERENCES 127 APPENDIX 140 8

9 ABBREVIATIONS Aa2 AD-test AHI ASDA ASDARI AwakeI Bf-S CAP Cz C4/A1 C3/A2 C4-M1 C3-M2 DL EDS EEG EMG EMGS EMGW EOG EOG P8-M1 EOG P18-M1 ESS FFT FM-test Fpz Fz Fp1-M2 Fp2-M1 Hz ICSD LatCon5-15 LatCum5-60 LatDL LatHYP LatSEM LatS1RKS LatS1VASS LatS2RKS LatS2VASS LatVASS mari Max mess Min MMSE MSLT MT MTS Alpha arousal from S2 Alphabetical Cancellation Test Apnea-hypopnea index American Sleep Disorders Association Arousal index according to ASDA > 30 s awakenings / h of sleep Well-being Scale Cyclic Alternating Pattern Electrode position at the vertex Electrode derivation right central referred to left earlobe Electrode derivation left central referred to right earlobe Electrode derivation right central referred to left mastoid Electrode derivation left central referred to right mastoid Drowsy-low Excessive daytime sleepiness Electroencephalography Electromyography, muscle tonus EMG activity in sleep EMG activity in wakefulness Electro-oculography EOG derivation above right eye referred to left mastoid EOG derivation below left eye referred to left mastoid Epworth Sleepiness Scale Fast Fourier Transformation Gruenberger Fine-motor Test Midline fronto-polar EEG electrode position Midline frontal EEG electrode position Electrode derivation left fronto-polar referred to right mastoid Electrode derivation right fronto-polar referred to left mastoid Cycles per second International Classification of Sleep Disorders Mean latency to the beginning of 5-15 s of continuous sleep Mean latency to 5-60 s of cumulative sleep Mean latency to DL Mean latency to first hypopnea/apnea if present before S1RKS Mean latency to first appearance of SEMs Mean latency to S1RKS Mean latency to S1VASS Mean latency to S2RKS Mean latency to S2VASS Shorter of LatDL/LatS1VASS Modified arousal index Maximum Modified Epworth Sleepiness Scale Minimum Mini Mental State Examination Multiple sleep latency test Movement time Movements in sleep 9

10 MTW Movements in wakefulness M2-M1 Electrode derivation between mastoids NREM sleep Non-REM sleep ODI 4 Oxygen desaturation index (> 4 %) OSAS Obstructive sleep apnea syndrome Oz Midline occipital EEG electrode position O1-M2 EEG derivation left occipital referred to right mastoid O2-M1 EEG derivation right occipital referred to left mastoid PLMS Periodic limb movement syndrome PSG Polygraphy PSQI Pittsburgh Sleep Quality Index Pz Central parietal EEG electrode position P4-O2 EEG derivation right parietal referred to right occipital QOL Quality of Life Index REM Rapid eye movement RKS Rechtschaffen and Kales scoring system RR interval Pulse interval RT-miss Misses in reaction time test RT-test Mean response time in reaction time test s Second SA Alpha-SEM, occipital alpha activity with SEMs SAF Alpha-SEM-F, diffuse or fronto-central alpha with SEMs SaO 2 min Minimum oxygen saturation SAS Zung Anxiety Scale SDS Zung Depression Scale SEI Sleep efficiency index SEM Slow eye movement ShInd Stage shift index ShiRKS Stage shift index in RKS ShiVASS Stage shift index in VASS SOL Sleep onset latency SPT Sleep period time SREM REM sleep, stage REM SWS Slow wave sleep, stages S0 Stage 0, wakefulness S1 Stage 1 S2 Stage 2 S3 Stage 3 S4 Stage 4 S0RKS S0 in RKS S1RKS S1 in RKS S2RKS S2 in RKS S1VASS S1 in VASS S2VASS S2 in VASS TIB Time in bed TST Total sleep time WA Wake-alpha, occipital alpha activity without SEMs WAF Wake-alpha-F, diffuse or fronto-central alpha without SEMs VASS Visual Adaptive Scoring System VIGdiff Difference between third and first part of vigilance test Vigil Mean response time in vigilance test WL Wake-low 10

11 1. INTRODUCTION The standardised scoring system developed by the Committee led by Allan Rechtschaffen and Anthony Kales was introduced to reduce variability in sleep recordings and analysis. This includes the standardisation of recording techniques, stage definitions and terminology. The scoring system of Rechtschaffen and Kales (RKS, 1968) provided increased reliability and consistency in sleep analysis and enabled the use of quantitative sleep parameters. The system is still widely used as a gold standard both in research and clinical practice. According to RKS sleep is divided into four non-rem stages and stage REM. The method was designed for paper recordings leading to the use of fixed epochs lasting 20 or 30 s. This type of classification with discrete stage definitions is especially problematic in the multiple sleep latency test (MSLT). Sleep onset does not take place abruptly; vigilance fluctuates between wakefulness and sleep as well as subvigilant stages before the subject falls asleep (Roth 1961, Ogilvie and Wilkinson 1984, Badia et al. 1994). The long epoch does not allow a description of this dynamics of sleep onset. The stage categories are also too few for the presentation of the various drowsiness states. MSLT is widely used to expose excessive daytime sleepiness. The associations between MSLT defined sleepiness and other objective measures of sleepiness have been variable (Johnson et al. 1990, Harrison and Horne 1996a). Similar results have been obtained when MSLT scores have been correlated with psychometric parameters and subjective assessments reflecting sleepiness (Pressman and Fry 1989, Chervin et al. 1995). Low MSLT scores indicating increased sleepiness have been found in subjects without vigilance complaints (Roth et al. 1980, Manni et al. 1991, Harrison and Horne 1996a, Geisler et al. 1998). Hori has divided wakefulness and light sleep into 9 stages (Hori et al. 1991, Hori et al. 1994). This division enabled an improved description of the sleep onset period from alert wakefulness to sleep stage 2, which is considered true sleep but Hori s staging is based on the use of fixed epochs. Temporal resolution can be improved by adaptive segmentation (Praetorius et al. 1977). The methods have until now been computerized and the applications in sleep research limited. New methods for analysing the sleep process and sleep onset are required. These should be based on more detailed stage definition and adaptive segmentation. This is also important for increasing the validity of automatic sleep analysis. The new approach might also improve the sensitivity and specificity of MSLT. 11

12 2. REVIEW OF THE LITERATURE 2.1. EVOLUTION OF SLEEP EEG DESCRIPTION Early days of sleep staging Loomis and co-workers were the first to describe different states of sleep based on the electroencephalogram (EEG, Loomis et al. 1937). They presented five sleep states characterised by differences in EEG potentials: A - alpha state, alpha rhythm in trains with or without slow rolling eye movements. B - low voltage state, no alpha rhythm, a fairly straight record with or without rolling slow eye movements (SEMs). C - spindle state, 14 Hz sleep spindles every few seconds. D - spindles plus random state, spindles with slow large random potentials up to 300 µv high. E - random state, the large random potentials coming from all parts of the cortex, spindles become inconspicuous. The stages were suitable for high-alpha producers, with non-alpha type individuals it was more difficult to distinguish between states A and B. States were noticed to shift continuously upward and downward. State E was most solid for different kinds of stimuli. Changes in the states could, however, occur even without any external stimulus. Two EEG derivations were used, one central and one occipital. Some topographical differences were noticed between EEG electrodes. Alpha activity seemed to be usually of greatest amplitude occipitally, but could be highest frontally. Spindles were most marked on the top of the head and random patterns came from all parts of the head. The authors also paid attention to the changes in alpha activity even during wakefulness. Moreover, they observed that in the dark, alpha activity could persist even if the eyes were opened. Dreaming was noticed to occur in state B. In 1953 a new type of eye movement in sleep, rapid eye movements (REMs), was described by Aserinsky and Kleitman (1953). They found 3-4 periods with REMs in a night sleep and paid attention to the cyclicity with which these periods occurred. They were able to combine the periods with REMs to dreaming and also to the changes in the activity of the autonomic nervous system. Shortly afterwards Dement and Kleitman (1957) introduced the cyclic patterns of sleep stages with cyclic periods of REMs as well. They divided sleep into four stages with stage 1 corresponding to the state B and partly to the state A of Loomis. Stage 2 corresponded to state C, stage 3 to state D and stage 4 to state E. Amplitude and frequency criteria for delta waves were implemented. Scoring could be performed as an on-line procedure. According to their results sleep stage 1 could be divided into two entities; first, stage 1 is seen at sleep onset, and second, cyclically through the night combined with REMs. Dreaming was seen in stage 1 with REMs whereas in stage 1 at sleep onset hypnagogic reveries might occur. The cyclicity of stages was visualised by hypnograms. Sleep stages were noticed to form cycles where some stages often followed each other while some stage changes occurred very seldom (Williams et al. 1964, Williams and Williams 1966). The slowest and highest waves were usually seen in the first cycle. Stage 3 and especially stage 4 were only occasionally seen during the second half of the night (Dement and Kleitman 1957). These 12

13 four non-rem (NREM) stages and stage REM (SREM) formed the basis of subsequent polygraphic sleep studies Further development of the sleep process Feinberg (1974) was one of the first to provide a detailed description of sleep cycle changes with age. Sleep stage 4 dominates the early cycles, especially in younger age groups. In older age groups the amount of stage 4 is diminished, but stage 3 seems to increase. Feinberg s conclusion was that stage 3 represents the same process as stage 4, but in a weaker version. Age did not have an effect on SREM period duration. As the decline of slow wave activity over consecutive sleep cycles was well documented and slow wave sleep seemed to have an increasing trend after sleep deprivation (Webb and Agnew 1971) Feinberg presented a theory that the purpose of stage 4 was to reverse the metabolic or neuronal effects caused by wakefulness (Feinberg 1974, Feinberg et al. 1984). Thus the longer wakefulness before going to sleep, the more intense slow wave sleep. This was also supported by the finding that late daytime napping reduces stage 4 the following night (Karacan et al. 1970). The alternating pattern between REM and NREM sleep was explained by the higher priority of stage 4 sleep. With sleep the demand for stage 4 is reduced and the demand for SREM exceeds it. That is when SREM can begin. Later SREM gives way to the greater demand for slow wave sleep. The two-process model of sleep regulation presented by Borbély a few years later is based on the assumption that instead of one, there are two separate processes underlying sleep regulation (Borbély 1982, Borbély 1984). One is a sleep-dependent homeostatic process, Process S, which rises exponentially during waking and shows an exponential decline during sleep. It corresponds to the declining trends of both slow wave sleep and the slow wave activity of sleep EEG (Borbély et al. 1981, Borbély 1982, Brunet et al. 1988). The other process is the circadian process, Process C which is independent of prior sleep and waking. The determining role of time of day in sleep regulation was demonstrated by Åkerstedt and Gillberg (1981). The phase position of Process C was based on the work of Åkerstedt and Fröberg (1977). Sleep propensity is determined by the difference between the two processes. As NREM sleep propensity is a reflection of the homeostatic process, REM sleep propensity can be considered a reflection of the circadian process (Borbély 1984) NEUROPHYSIOLOGICAL BASIS OF NREM SLEEP AND NREM SLEEP EEG Slow oscillation Recent experimental animal studies have shown that the principal process behind NREM sleep is slow oscillation of < 1 Hz (Steriade et al. 1993a, Achermann and Borbély 1997, Amzica and Steriade 1998a, Steriade and Amzica 1998a). The frequency of slow oscillation varies between Hz, the depolarising phase lasting for s while the hyperpolarization lasts for s (Steriade and Amzica 1998b). The slow oscillation seems to be generated in the cortex, since it is present in athalamic cats (Steriade et al. 13

14 1993b) but in decorticated cats it cannot be seen (Timofeev and Steriade 1996). The depolarising component is generated out of excitatory and inhibitory synaptic potentials (Steriade and Amzica 1998a). The hyperpolarization is associated with decreasing firing of the cortical network (Steriade and Amzica 1998a), being associated with decreased facilitation of the network (Contreras et al. 1996). The slow after-hyperpolarization is abolished by acetylcholine in cortical slices in vitro (McCormick and Prince 1986). In vivo sensory input via the brainstem systems can interfere with slow oscillation (Steriade et al. 1993d). Slow oscillation is synchronised widely over the cortical surface by intracortical linkages (Amzica and Steriade 1995). It is also described in thalamocortical and thalamic reticular neurons (Steriade et al. 1993c) and at least some other brain sites Impact of slow oscillation on cortical EEG The appearance of various EEG rhythms and patterns in NREM sleep can be explained by slow oscillation. The hyperpolarizing-depolarising sequence on the cellular level gives rise to a K complex (KC). Several KCs together at the EEG level display the rhythmicity of the slow oscillation (Amzica and Steriade 1997). At sleep onset the constant excitatory input via the midbrain reticular formation and mesopontine cholinergic nuclei to the thalamus and cortex is reduced, allowing the hyperpolarization of thalamocortical cells to occur (see Amzica and Steriade 1998a). This leads to the increasing sensory deafferentation of the cortex. In this state of vigilance the EEG shows decreased amplitude, and, according to Amzica and Steriade (1998a), no sleep oscillations are present. At this time the synchronisation and the level of the slow oscillation of the cortical network is low. Some vertex waves can be seen in the EEG. With further synchronisation and spreading of the slow oscillation over the cortical surface the vertex waves become more visible and their amplitude increases until they assume the form of the KCs (Amzica and Steriade 1998b). The incidence of KCs expresses the increasing synchronisation and with deepening of sleep and sensory deafferentation KCs become more rhythmic (Amzica and Steriade 1997). Thus spontaneous KCs can be regarded as an oscillatory phenomenon in contrast to the evoked KCs (Amzica and Steriade 1998b). Widening slow oscillation spreads to the thalamus in synchronous volleys. In the thalamus it starts to drive spindles produced by thalamic reticular and thalamocortical cells (Morison and Basset 1945) to the cortex through the thalamocortical network (Steriade et al. 1990, Contreras and Steriade 1996, see Amzica and Steriade 1998b). Thus during light sleep every cycle of slow oscillation usually leads to a spindle sequence (Steriade and Amzica 1998a). The spindles are considered to be sleep maintaining events (Naitoh et al. 1982, Jankel and Niedermeyer 1985) which block the transfer of sensory information into the cortex allowing the evolution of sleep into deeper stages (see discussion after Steriade 1992, Timofeev et al. 1996). In this sleep phase the cortical EEG shows KCs and spindles (Amzica and Steriade 1998a). 14

15 Thalamocortical neurons can also generate clock-like oscillations with the frequency of 1-4 Hz (McCormic and Pape 1990, Leresche et al. 1991, Steriade et al. 1991). With increasing hyperpolarization of the thalamocortical cells spindles are gradually replaced by this intrinsically generated delta activity (Steriade et al. 1991). The slow oscillation also synchronises this activity to spread all over the cortex (Amzica and Steriade 1997, Amzica and Steriade 1998b). This state corresponds to the slow wave sleep of humans (stage 3). In stage 4 of human sleep the hyperpolarization level is highest (Amzica and Steriade 1998a). Cortical neurones can also generate delta activity. This delta activity may also be synchronised by the slow oscillation. The different patterns together result in the complex waveforms seen in the EEG during NREM sleep (see Steriade and Amzica 1998a). That slow oscillation and delta activity are also two distinct entities in humans has been demonstrated by Achermann and Borbély (1997). Steriade has described a double frequency for spindles in cats. Spindles consist of the activity between 7-14 Hz lasting for 1-2 s repeating periodically at a frequency of Hz (Steriade 1993, Steriade 1994). In humans spindles follow one another at intervals of about 4 s (Kubicki et al. 1986, Spieweg et al. 1992, Evans and Richardson 1995, Achermann and Borbély 1997). This 4 s periodicity (0.25 Hz) is somewhat slower than the frequency band of slow oscillation. Achermann and Borbély (1997) point out that if this same slow mechanism explains the 4 s periodicity, the phase of hyperpolarization must be longer in humans than in cats. Whether or not the periodicity of the spindles is an expression of slow oscillation in humans is still obscure. The two-process model of Borbély and coworkers (see above) implies that there is a homeostatic and a circadian component, which regulate the sleep/wake and NREM/REM interactions. The question remains whether hyperpolarization with slow oscillation is the true representative of the homeostatic component Other periodicities in sleep Besides the slow oscillation several other periodicities exist in sleep. The most prominent is the above-mentioned min. sleep cycle, which was originally described by Dement and Kleitman (1957). It has been proposed that the NREM-REM sleep cycle is generated by the reciprocal interaction of REM-on and REM-off neurons in the brainstem (McCarley 1994). Shorter periodicities can also be found in normal sleep. Lugaresi at al. (1972) described several vegetative and somatic phenomena (systemic arterial pressure, pulmonary arterial pressure, cardiac rate, arteriolar tone, breathing, peripheral motoneurone excitability, level of consciousness) which tend to oscillate or repeat themselves periodically every s especially during light sleep. Roth (1961) paid attention to the EEG fluctuations during sleep onset. The durations of the stages with stationary EEG activity varied between 2-20 s. The periods were shorter at the beginning of the vigilance fluctuation, lengthening with time. Continuous short state changes during lowered vigilance and at sleep onset were also noticed by Oswald (1962). 15

16 The fluctuating nature of sleep onset can be visualised by scoring with short 5 s epochs (Badia et al. 1994). It has been postulated that the beginning of sleep in fact consists of multiple sleep onsets (Ogilvie and Wilkinson 1984). On the other hand sleep onset can be considered to consist of brief microsleeps which usually appear as close clusters (Guilleminault et al. 1975). Evans also studied the naturally occurring fluctuation of EEG measured vigilance. She observed that the EEG episodes at sleep onset consisted of periods of higher arousal associated with a shift towards wakefulness and periods of lower arousal with a shift towards sleep (Evans 1992, Evans 1993). In stage 1 the dominant interval of the alternating alpha and theta periods was 16 s with other possible peaks of about s and s. In deeper NREM sleep (stage 2, stage 3 and stage 4) the sleep stages alternated with arousals. Definite Arousals in stage 2, stage 3 and stage 4 were defined by the presence of 4 of the following patterns: alphaburst, one or more KCs, SEMs, an increase in EMG activity, a change in respiration and/or beat-to-beat heart rate. The definition used for arousals was more liberal than is suggested by the atlas task force of the American Sleep Disorders Association (ASDA 1992). In the ASDA criteria arousal is defined by a shift in EEG frequency to theta, alpha and/or faster frequencies, but not spindles. In Evans study the dominant interval between higher and lower arousal states in stage 2 was s. Definite arousal activity was sparser in stages 3 and 4. The intervals between definite arousals lengthened as sleep became sounder. On the grounds of this work sleep stage 1 could be seen as an alternating pattern of alpha and theta activity instead of a discrete stage. Three dominant slow EEG periodicities of theta, alpha and beta power bands have also been observed during wakefulness (Novak et al. 1997). The fastest, with a periodicity of 10 s was related to the respiratory rate. The two other dominant rhythms had periods of s and about 46 s. The two fastest periodicities are close to those observed in RR intervals. Thus it seems that EEG periodicities are related to the functions of the autonomic nervous system. All three periodicities correspond to those found by Evans (1992, 1993) during sleep onset. The slowest periodicity is close to the cyclic alternating pattern (CAP). Schieber and co-workers have described the alternation between sleep and short arousals as les phases d activation transitoire, which are regarded as being part of the normal sleep process (see: Muzet et al. 1991, Paiva and Rosa 1994). Frequent short micro-arousals in light sleep decreasing in deeper sleep have been observed by Halász et al. (1979) Cyclic Alternating Pattern Terzano and co-workers have introduced the cyclic alternating pattern (CAP, Terzano et al. 1985). CAP is periodic EEG activity consisting of two different alternating EEG patterns. It can be divided into two phases, phase A and phase B, each lasting more than 2 s and generally less than 60 s. This cyclicity is related to fluctuation of vigilance between two sleep levels. The two phases represent different morphology and different responsiveness to stimulation. By stimulus experiments phase A has been proven to represent the higher arousal level. CAP phases tend to recur at 20 to 40 s intervals. One 16

17 CAP cycle consists of phase A followed by phase B (Terzano et al. 1985, Terzano and Parrino 1991). Originally cyclic alternation of EEG patterns was described in comatose patients and in Creutzfeldt-Jakob disease (Evans 1975, Terzano et al. 1981). CAP phases have different morphology depending on the sleep stage in which they occur. Microarousals in all stages are taken into account as phase A. In sleep stage 1 phase A consists of alpha activity or vertex wave sequences and phase B corresponds to the disappearance of the alpha activity. In stage 2 phase A consists of KC sequences or KCs with alpha-activity. In stages 3 and 4 phase A consists of delta bursts (Parrino et al. 1996). No equivalent rhythmic pattern has been found in normal REM sleep, but with severe sleep fragmentation, as in obstructive sleep apnea syndrome, CAP sequences may also be present in REM sleep (Parrino et al. 2000). Functionally CAP phase A events correspond to transient arousals (Parrino and Terzano 1996). CAP extends the arousal definition formulated by ASDA (1992). Many of the CAP phase A patterns are not included in the ASDA arousal criteria. The minimum duration request of an ASDA arousal is 3 s while that of CAP phases is 2 s. CAP rate is defined as a percentage ratio of total CAP time in NREM sleep to total NREM sleep time. In young adults CAP rate is normally about 23%. It is age-related, expressing a minimum in young humans, increasing with age (Terzano and Parrino 1993). With high sleep pressure CAP rate may diminish, but it never approaches zero (see Terzano and Parrino 1991). Normally CAP sequences occur at sleep onset and after nocturnal awakenings (Terzano et al. 1988). They are also abundant in S2 preceding REM sleep (Terzano et al. 1985). This cyclic fluctuation of arousal is connected with alternations in autonomic functions (e.g. heart rate, respiratory activity). In a recent study heart rate variability related to CAP A phases resulted in a significant increase of low frequency components with a decrease of high frequency components (Ferini-Strambi et al. 2000). This reflects the shifting of the balance of the autonomic nervous system towards sympathetic activation. This was the case even when the conventional arousals were excluded. CAP can be seen as a marker of instability in the sleep process. As the morphology of phase A is the same for both external and internal stimuli, it can be seen as a response to both internal and external messages (Terzano et al. 1988). CAP may be a sign of the reorganisation of the brain so that it can respond to changes in environmental conditions (Terzano et al. 1985). The cyclic, recurring character of CAP could suggest the existence of natural arousal rhythm in the EEG of sleep. To summarise, it seems evident that the macrodynamics of the sleep process consists of continuously alternating microstates (Halász and Ujszászi 1991). These microstates form naturally occurring oscillations and periodicities within sleep. Their internal relationships, especially the possible connections between slow oscillation and CAP are still unclear. 17

18 2.3. STANDARDISED SCORING SYSTEM Rules of the standardised scoring system Since 1968 visual sleep stage scoring has been performed according to the guidelines established by the committee led by Allan Rechtschaffen and Anthony Kales. The need for adjustment of scoring rules was disclosed by the study of Monroe (1967) in which he showed that the inter-rater agreement of scorings between different laboratories was low. The aim of A manual of standardised terminology, techniques and scoring system for sleep stages of human subjects often called the manual of Rechtschaffen and Kales (RKS), was to increase the comparability of results from different laboratories (Rechtschaffen and Kales, 1968). The manual was based on the psychophysiological knowledge of the sleep process at that time. It provides the minimum requirements for polygraphic sleep studies of adult humans. It also provides the classification of sleep stages, standardised terminology and definitions of different parameters obtained from recordings. The guidelines of RKS were designed for paper recordings. The manual contains definitions for filters, gains, paper speed, pen deflection, and number of channels, etc. This was very important because the settings affect amplitude, frequency and phase of measured signals. Some of the requirements and instructions are no longer necessary during recording with the increasing use of digital polysomnographs in which the parameters can be adjusted afterwards. A single optimal EEG derivation was specified for recordings; either the C4/A1 or C3/A2 can be used. Eye movement recording with two electrodes produces out-of-phase deflections by eye movements as EEG activity in eye movement channels is seen as inphase deflections. Submental EMG is recorded to differentiate the decreased muscle tonus in SREM. The minimum amount of channels that is required for one recording is four. The definitions of the five sleep stages are essentially based on the stage classification of Dement and Kleitman (1957). Sleep stages are characterised by a specific set of variables, which are composed of different kinds of EEG patterns with eye movement and muscle tonus patterns. Wakefulness stage (S0) consists of EEG alpha activity and/or low voltage, mixed frequency activity. Stage 1 (S1) is defined by low voltage, mixed frequency EEG without rapid eye movements. Vertex waves can also be seen. Stage 2 (S2) consists of Hz sleep spindles and KCs on a relatively low voltage, mixed frequency background. The minimum duration for spindles and KCs was set at 0.5 s, and the amplitude demand for KCs was 75 µv. Stage 3 (S3) consists of moderate amount (20-50%) of high amplitude slow wave activity. The amplitude must exceed 75 µv, and the frequency of that slow activity had to be < 2 Hz. Stage 4 (S4) consists of large amounts (> 50%) of high amplitude slow wave activity. Stage REM (SREM) shows low voltage, mixed frequency EEG with saw-tooth waves, episodic REMs and low amplitude submental EMG. Movement time (MT) was defined as an additional score for the situations where amplifier blocking or muscle activity in one epoch makes sleep staging impossible. 18

19 The most common parameters derived by sleep stage scoring are the time in bed (TIB), sleep efficiency index (SEI), sleep onset latency (SOL) and the percentages of sleep stages. Graphically the sleep process can be displayed as a sleep histogram, the hypnogram. Both the numerical and graphic results are used for the differentiation between good and disturbed sleep. They are also used as parameters in various psychophysiological and pharmacological experiments. The inter-scorer agreements for S2 and SREM have usually been quite high, whereas the agreements for S1 and S3 have been much lower. Agreements found in different studies have been % for S0, % for S1, % for S2, for S3 and % for S4 depending on subject groups and whether the comparisons were made within or between laboratories (Martin et al. 1972, Kuwahara et al. 1988, Kubicki et al. 1989, Kim et al. 1992, Schaltenbrand et al. 1996). In a recent study among eight European sleep laboratories the inter-scorer reliabilities (Cohen s Kappa) for healthy subjects were for wakefulness, for S1, for S2, for SWS and for SREM (Kunz et al. 2000). For patients with different sleep disorders lower kappa values have been obtained (0.723 for wakefulness, for S1, for S2, for SWS and 0.82 for SREM, Danker-Hopfe et al. 2000). The low inter-scorer agreements in visual sleep stage scoring increase the variability and uncertainty of the results obtained by the method Evaluation of the standardised sleep stage scoring system Scoring with epochs ignores short lasting oscillations Sleep stage scoring in RKS is performed in epochs of equal lengths. The whole epoch is scored as one stage. If signs of two or more stages are present the epoch is assigned to the stage whose landmarks are of longest duration within the epoch. In standardised sleep stage scoring the fluctuations of vigilance must be ignored. Epoch scoring enables the convenient analysis of paper recordings. Results become easy to handle when one page is scored into one stage. The one-page epoch also minimises the time used for later parameter calculations. The chosen paper speed with an epoch length of 20 or 30 s can be seen as a compromise between accuracy and laboriousness. The hypnogram does not provide a proper description of the underlying sleep/wakefulness continuum (Pardey et al. 1996a). With epoch scoring the min. cyclicity is revealed, but the shorter periodicities described above are not well visualised. The physiological fluctuation of the vigilance states within an epoch remains unnoticed. Even if RKS scoring is supplemented by arousal scoring (ASDA 1992) the microstructure of sleep is not visualised properly (Terzano et al. 1985, Terzano and Parrino 1991). An excessively smooth picture of the dynamics of the sleep process results from RKS. Topographical aspects With standard sleep scoring rules only central leads are taken for the classification. Using only one EEG derivation means that only a minor part of the brain surface is evaluated. The occipital localisation of the alpha activity was, however, pointed out already in 1949 (Brazier 1949). On the other hand Jasper and Andrews (1938) had paid attention to 19

20 another alpha activity, which was precentrally located and was not necessarily blocked during light stimulation. The frequency of the wakefulness-related occipital alpha rhythm is 8-12 Hz. More recent work has disclosed that the other alpha activity is an approximately 2 Hz slower centro-frontal rhythm and is present during lower vigilance state (Kubicki et al. 1985, Broughton and Hasan 1995). Theta activity reaches its maximum in the central regions (Broughton and Hasan 1995). The amplitude of the delta waves is highest frontally. Therefore the frontal delta waves can show S4 sleep pattern at the same time as delta waves in central leads are too low to be scored as S4 (Kubicki et al. 1985). The vertex waves, KCs and most often the sleep spindles are of higher amplitude in the midline than in the C3-A2, C4-A1 derivations which are recommended in the manual (Broughton and Hasan 1995). About one third of the KCs can be seen only by frontal leads, whereas one third is seen by central leads and one third by both leads (Paiva and Rosa 1991, McCormick et al. 1997). Two different types of sleep spindles have been observed. The faster spindles which have a frequency of about 14 Hz are parietally located, whereas somewhat slower 12 Hz spindles are frontal. Spindles recorded by the recommended electrodes probably represent a mixture of spindle activity in the frontal and the parietal area (Jobert et al. 1992). Some of the spindles are visible only parietally or frontally (Kubicki et al. 1985) and are thus totally ignored by the standard system. Sawtooth waves show their voltage maximum at the vertex (Broughton and Hasan 1995). Some arousals may be visible only frontally and are not visualised by the standard system (O Malley et al. 1996). RKS does not take into account the continuity of the sleep process and phasic events Scoring of S2 is heavily dependent on the presence of the phasic events, the spindles and KCs. According to the standardised scoring rules S2 begins when the first well-defined spindle or KC appears. However, no unambiguous definitions for the phasic events exist (Broughton 1991). The definition of the KC by morphology is difficult. KC is defined as a high-amplitude (>75 µv) EEG pattern having negative sharp wave, which is immediately followed by a positive wave. The start and endpoints of individual KCs are not clearly stated in any of the definitions. In the manual of RKS the minimum duration of a KC is specified to be 0.5 s. However, Amzica and Steriade (1997) found the duration of KCs to vary from s to s. The minimum duration of a spindle has to be 0.5 s (Rechtschaffen and Kales 1968) but the minimum amplitude is not stated. Usually the amplitude of spindles and KCs increases gradually at sleep onset. The decision whether a spindle or KC is sufficiently well-defined is not easy and depends on the experience of the scorer. The spindles and KCs represent sleep microstructure, but the number and rate of these phasic events in no way affects sleep stage scoring unless there is a longer than 3 min. pause in their appearance. But, as spindles are considered sleep protective, their rate of occurrence may also have an impact on sleep quality. 20

21 S2 is easy to score as long as regularly occurring phasic events are present. Problems arise when phasic events are sparse or when the amplitude of the background becomes higher. By definition spindles and KCs should appear on a low voltage background activity. The differentiation between S2 and S3 as well as the differentiation between S3 and S4 is made by the percentages of slow wave activity within an epoch. This differentiation system can be considered artificial (Lairy 1977). The amplitude of the slow wave activity must exceed 75 µv, which can cause problems with visual scoring. The issue of the amplitude criterion was discussed by the Committee, but the criterion was upheld. The 75-µV threshold seems to work very well with young people. Delta activity, however, declines with age (Webb and Dreblow 1982, Feinberg et al. 1984). Therefore a high amount of S2 and very low amounts of S3 and S4 are scored in the recordings of elderly subjects even if they have no sleep complaints. As a consequence a hypnogram of a healthy elderly person may resemble a hypnogram of a patient suffering from poor sleep, for instance, insomnia or sleep apnea because S2 generally increases and S3 + 4 also decreases in sleep disturbances (ICSD 1997, Figure 1). In RKS sleep often looks more normal than it in reality is and the actual pathological phenomena may be lost. It is possible that, for example, completely different morphological states are scored as S2. This was pointed out already in 1977 by Sträle (Lairy 1977). In apnea patients S2 could be seen as a misleading term because the S2 of an apnea patient is constantly interrupted by the periodic arousals and the microstructure of sleep looks quite different from the normal S2 (Guilleminault et al. 1975). RKS is not sufficient to describe drowsy states The division into RKS stages ignores slight impairment of vigilance leaving drowsiness states undefined. However, early studies already demonstrate different polygraphic patterns associated with drowsiness. Simon and Emmons (1956) found a systematic change in the alpha activity with decreasing vigilance; the amplitude of the fast wakefulness alpha starts to decline with increasing drowsiness. This is followed by occasional alpha attenuation. Further decrease in vigilance is associated with slowing down of the frequency of the alpha activity by 2 Hz, later this slower alpha starts to attenuate and finally disappears. Hori et al. (1991, 1994) divided the conventional sleep stages S0, S1 and S2 into 9 different stages by EEG morphology. Stages 1-3 are separated from each other by the amount of alpha activity, stage 4 consists of low voltage activity, stage 5 consists of ripples, stage 6 is characterised by occasional vertex waves, in stage 7 vertex bursts are seen, incomplete spindles appear in stage 8, and in stage 9 well formed spindles are seen. Stages 1-2 correspond to wakefulness in RKS, stages 3-8 correspond to S1 and stage 9 corresponds to S2. The stages could be differentiated from each other by the auditive reaction time test. In RKS rules the vigilance state is determined mainly by EEG changes. SEMs are not included in the rules although they are stated to be present in S1. With open eyes SEMs with alpha activity can be regarded as a sign of lowered vigilance (Valley and Broughton 1983, Torsvall and Åkerstedt 1987). With closed eyes the appearance of SEMs can 21

22 precede the attenuation of the alpha activity by several seconds (Liberson and Liberson 1965). Kojima et al. (1981) divided vigilance into 4 different states based on the frequency, distribution and continuity of the alpha waves. They observed that in alert wakefulness REMs were more frequent, but with increasing slowing down of the frequency and widening of the distribution as well as decreasing of the continuity of the alpha activity, SEMs became more common and REMs were reduced. The appearance of SEMs with alpha activity can be taken as an early sign of drowsiness also with closed eyes (Santamaria and Chiappa 1987, Ogilvie et al. 1988). Figure 1. Sleep disorders cause non-specific changes of hypnograms scored by RKS. Therefore the nature of the disorder cannot always be identified by the hypnogram alone. It may also be difficult to differentiate between patients and healthy elderly subjects without sleep complaints. At the top a hypnogram of a young patient with sleep apnea syndrome. He also suffers from insomnia. In the middle and at the bottom hypnograms of healthy well-sleeping elderly subjects without sleep complaints. The patient seems to sleep quite continuously, presenting no slow wave sleep. In the middle the elderly subject has practically no slow wave sleep. The subject on the bottom has slow wave sleep, but also frequent awakenings fragmenting sleep. 22

23 Atypical PSG patterns The rules of RKS are suitable for normal, common sleep EEG characteristics. With abnormal or deviant normal electrophysiological patterns problems with scoring arise. Alpha-delta sleep is defined by the persistence of alpha activity during NREM sleep (Hauri and Hawkins 1973). Moldofsky (1990) has suggested that the alpha-delta pattern is a reflection of nonrestorative sleep of fibromyalgia patients but it can also been seen in healthy good sleepers (see Pivik and Harman 1995). As there are no rules for scoring this pattern it is unclear how it should be classified. If alpha activity during sleep is abundant it may considerably hamper visual scoring. The determination of sleep onset, arousals and longer wakefulness episodes within sleep may prove difficult. With low-alpha subjects the problem is the reverse: the attenuation of alpha activity cannot be used as a marker of S1 and one has to rely on the theta activity. In parasomnias EEG sleep patterns, even delta waves, can be seen with concomitant body motility, high EMG tonus and sometimes with eye movements (Broughton 1993, Schenck et al. 1998). In the standard scoring system there are no rules for scoring such a state. The epochs with parasomnias might be scored as wakefulness, movement time or one of the NREM stages, depending on the scorer and the electrophysiological picture. In narcolepsy elevated submental muscle tonus can be seen during REM sleep (Schenck and Mahowald 1992). Scoring such cases the state with SREM alike EEG with persistent muscle tonus could be wrongly indexed as wakefulness or S1. On the other hand the onset of SREM periods could be delayed if no decrease in submental EMG occurred in epochs with REM like EEG preceding the appearance of the first REMs (Kubicki et al. 1985). REM sleep behavior disorder is characterised by elevated submental muscle tonus and excessive chin or limb movements (for a recent review see Sforza et al. 1997). Corresponding scoring problems to those of recordings from subjects with narcolepsy and parasomnia may emerge. The REM sleep episodes could be scored as wakefulness, SREM or sometimes partly as S1. Especially during the first SREM period both SREM and S2 related phasic events may occur quite close to each other. This phenomenon was already described by Dement and Kleitman (1957), who called it "slipping" down from SREM to S2. It can be seen in 1-8% of normal subjects (Broughton 1993). The reason for this phenomenon might be a very rapid alternation of stages, but the other possibility is that SREM and NREM related brain processes could coexist. With a long standard epoch the scoring of these episodes becomes difficult. Summary of the criticism of RKS Some of the shortcomings of the standardised scoring system can be summarised as follows. Scoring with the RKS rules in many cases requires subjective judgement of the EEG, which can lead to unreliable results. Due to epoch-thinking all short-lived changes in the signal are masked although they could be the most interesting from the clinical point of view (Pardey et al. 1996a). Also, if epoch-scored vigilance states are used together with a 23

24 reaction time test, misleading results may be obtained (Ogilvie and Wilkinson 1988, Conradt et al. 1999). The EEG derivations recommended for standardised visual scoring are not optimal for any of the EEG events needed for sleep staging. Many events may be poorly visible even in normals because of their location. This may partly explain why differences in sleep parameters between healthy subjects and patients remain modest. As it is now, the parameters obtained by sleep stage scoring give a superficial picture of the differences between good and poor sleep. In addition to the need for more EEG derivations, a more detailed division of the sleep stages seems possible by EEG morphology. This is entirely in line with the opinion expressed by the Committee led by Rechtschaffen and Kales. They encouraged the use of other concepts wherever needed. Revisions of the manual were suggested with the acknowledgement of new information. However, there have been no systematic attempts to revise the rules Visual methods to supplement and improve RKS An occipital derivation can be used to record alpha activity in order to improve the differentiation between wakefulness and S1. According to Williams et al. (1974 p ) this is even necessary in most circumstances. They also recommended the use of a frontal channel for a more accurate detection of delta activity. RKS was not designed to address sleep disrupted by respiratory events. Wakefulness and sleep cannot be accurately estimated and total sleep time (TST) and apnea indices tend to become unsatisfactory. To avoid the problem of classifying fragmented sleep into normal sleep stages, transitional sleep (T-sleep) was defined (McGregor et al. 1992). T-sleep is used to separate the continuously alternating sleep and wake transitions of sleep apnea patients from normal sleep. T-sleep differentiates well between the periods of disrupted sleep and more normal sleep, but with T-sleep scoring the microstructure of disturbed sleep remains undescribed. The arbitrariness of the rules separating S2, S3 and S4 could be avoided if NREM sleep were be classified from the viewpoint of the spindles into spindle-dominant and deltadominant sleep (Naitoh et al. 1982). This could be easily done even visually. With this kind of scoring the microstructure of the sleep process would be better revealed than by RKS. Use of shorter epoch lengths can improve the temporal resolution of scorings. Shorter epochs have been used often in vigilance studies (Morrell 1966, Häkkinen 1972, Townsend and Johnson 1979, Valley and Broughton 1983, Belayavin and Wright 1987, Torsvall and Åkerstedt 1987, Värri et al. 1992) but seldom in night recordings (Weitzman et al. 1980). Additional scoring of arousals can reveal sleep fragmentation. Arousals are more frequent in disturbed sleep (Staedt et al. 1993). But as sleep consists of different periodicities, it is somewhat unclear what can be considered as normal fragmentation and when the 24

25 fragmentation is excessive (Martin et al. 1997). The ASDA arousal criteria (1992) can be seen, however, to be quite conservative and some patterns resembling arousals are thus omitted from calculations. As the minimum duration of the arousals must be at least 3 s, some short arousals are ignored. The inter-scorer agreement for arousal scoring is not always satisfactory (73% - 99%), which further increases the problem of arousal scoring (Stepanski et al. 1984, Roehrs et al. 1994, Sangal et al. 1997). Among the different visual methods, CAP scoring has proven the most successful. It has revealed a more fluctuating picture of the sleep process than can be obtained by the conventional scoring method. It has also provided a more specific description of normal sleep and sleep disturbances than RKS. CAP rate is increased by artificial sleep disruption such as by noise (Terzano and Parrino 1993). It is also clearly enhanced in sleep disturbances as in sleep apnea, periodic limb movements and insomnia (Terzano et al. 1996, Parrino et al. 1997). In insomniacs CAP scoring has turned out to be an efficient tool in drug experiments. Whereas conventional sleep parameters supply only limited information, the CAP parameters (microstructure) have shown high sensitivity to both drug and condition factors (Parrino et al. 1997) COMPUTERIZED SLEEP ANALYSIS Sleep stage scoring There have been attempts to develop automatic methods for sleep scoring for nearly 30 years. So far the only method that has been widely applied in sleep research is the hybrid system developed by Gaillard and Tissot (1973). The use of the system requires that all critical parts, for instance, sleep onset and stage shifts have to be re-examined afterwards by a visual scorer. On the other hand with this system the differentiation between S2, S3 and S4 is totally based on the computer program. One of the pioneers in the field is Smith and co-workers (Smith et al. 1975, Smith et al. 1978). In practice, the majority of even novel digital systems are based on the hybrid principles introduced by Gaillard and Tissot (1973) and Smith et al. (1978). The method implies that there are detectors for similar waveforms that are used in human scoring, for instance, spindles, delta activity and eye movements. The computer program combines the information obtained by the detectors and forms the stages. In young, healthy subjects the total agreement between visual and computer scoring has been found to be %. Agreements for wakefulness have varied % and for S %. Agreements from 45 to 97 % have been obtained for S2, % for S3, % for S4 and % for SREM (Martin et al. 1972, Gaillard and Tissot 1973, Kumar 1977, Smith et al. 1978, Hoffmann et al. 1984, Kuwahara et al. 1988, Kubicki et al. 1989, Schaltenbrand et al. 1996). There are few studies with man/machine agreement in subjects with sleep disorders. Lower total agreements, between 63.1 % and 70.5 % have been obtained than in subjects without sleep complaints (Hasan 1983, Sangal et al. 1997). 25

26 In general, automatic analysis is not considered sufficiently reliable for clinical use but the results have always to be re-examined by a visual scorer. It has been postulated that the problems of computerized sleep analysis are not due to the inadequacy of computer programming, but to the ambiguous definitions of sleep stages (Hasan 1996). More specific methods that follow the sleep process more closely are required. A step in this direction is the automatic detection of CAP, which seems promising (Rosa et al. 1999) Spectral analysis Automatic analysis systems can also be used to quantify different phenomena derived from polygraphic recordings. Among these methods the Fast Fourier Transform (FFT, Cooley and Tukey 1965) is evidently the most commonly used. FFT has been used both for attempts at computerized sleep stage scoring and as an independent tool for quantitative studies on sleep structure and dynamics. Almost 60 years ago Knott et al. (1942) presented their data with spectral power, which they called energy. They compared the energy of different frequency bands in wakefulness and in three sleep stages (low-voltage, spindles and spindles + random) following the criteria of Loomis et al. (1937). They noted the negative relationship between the activity in the 1-3 Hz and 8-12 Hz bands. Their conclusion was that there are changes in energy between wakefulness and sleep but no new frequencies appear in sleep as compared to wakefulness. However, a characteristic sigma peak during the two deeper sleep stages is clearly visible in their figures. The first spectral study of the sleep stages defined according to the Dement and Kleitman (1957) criteria was made by the group of Johnson (1969). The study was based on clearly defined, artifact-free 1 min. sections from each stage. They found an average peak frequency of alpha activity in wakefulness of 9.8 Hz. They also found a slower alpha peak in stage 1, although this is not clearly visible in their figure with the spectra. A sigma peak was stated to exist in all sleep stages but not in wakefulness. According to their view sigma activity was unique to sleep. In this respect they disagreed with Knott et al. (1942). The power of theta and delta activity increased with deepening of sleep but a large amount of slow activity was found in all stages. In a subsequent study the group made an attempt to discriminate between sleep stages scored according to Dement and Kleitman (1957) by EEG spectra (Lubin et al. 1969). The spectra derived by FFT from 1 min. sections for each stage were divided into 5 frequency bands: delta, theta, alpha, sigma, beta. Linear discrimination among the six stages by stepwise multiple regression was used in order to find a minimum set of EEG predictors that would give the best possible multiple correlation. The poorest result was obtained for stage 3, where the linear discriminator was almost imperceptible. Linear discrimination between selected cases gave better results but stage 1 and stage REM could still not be separated. The separation between stages 2, 3 and 4 was also unsatisfactory. It was suggested that this poor result was due to unreliability in visual scoring, which should therefore be replaced by quantitative criteria. It was also stated that the detection of phasic events might improve the results. 26

27 Later on several attempts were made to use spectral analysis as a basis for the discrimination between sleep stages scored according to RKS. One of the first attempts to develop a computer program for automatic sleep stage scoring of a whole-night recording was made by Martin et al. (1972). They first tried to apply spectral analysis to distinguish between S2, S3 and S4. As the results were poor they had to develop a specific computer algorithm for the detection of high-amplitude delta waves in order to replicate visual scoring. However, they believed that quantitative measurement of continuous slow wave activity was a more reliable and sensitive indicator of changes in slow wave sleep than the division into discrete stages. Molinari et al. (1984) used stepwise linear discriminant analysis of spectral parameters to distinguish between RKS sleep stages. The parameters used varied between subjects. The error rate was only %. Analysis showed that most errors occurred between wakefulness, S1 and SREM or between adjacent slow wave sleep stages. Molinari et al. suggested that in order to obtain a more precise description of the sleep process the excessively rough visual staging of NREM sleep should be replaced by a continuous model. The weakness of the method is that the same set of parameters was not applied for all subjects. A well-known problem in using FFT is that the method can give a higher spectral power for a large number of low-amplitude slow waves (< 75 µv) than for a few waves with an amplitude exceeding the 75-µV scoring criteria of the standard manual. FFT is, however, considered suitable for the quantitative study of the dynamics of the sleep EEG (Borbély et al. 1981). Already Martin et al. (1972) suggested that spectral analysis can provide an efficient an reliable tool for various sleep studies. Effects of sex and ageing on sleep EEG have been studied by FFT (Ehlers et al. 1998). Besides sleep macrostructure spectral features of arousals have been studied by FFT. Halász and Ujszászi (1991) noted that in NREM sleep an acoustic stimulus elicits a temporary increase in the power of nearly all frequency bands except for the Hz band in which a depression can be seen FFT in sleep-wake transition Several groups have used FFT to study the changes in spectral power during sleep onset. Spectral analysis provides a method to examine the microstructure by tracking spectral changes throughout the entry into sleep as well as in smaller vigilance changes during the day. EEG total power and the power in the Hz band have been shown to increase during sustained wakefulness (Cajochen et al. 1995, Corsi-Cabrera et al. 1996). A general increase in EEG total power as well as increases in all frequency bands have been observed at sleep onset with closed eyes (Ogilvie et al. 1991, Ogilvie and Simons 1992). There are two kinds of observations regarding alpha activity: decreases in alpha band power occur prior to visually scored sleep onset (Badia et al. 1994). On the other hand increases in alpha power band have been observed at sleep onset (Ogilvie et al. 1991). In another study by the same group the highest levels of alpha activity were shown to occur during the most alert responsiveness and during early behavioral sleep (Ogilvie and Simons 1992). Increase in theta band power has been observed already before S1 can be scored according to the conventional scoring criteria (Badia et al. 1994). A decrease in 27

28 the power of beta activity has been found to occur with the shift from wakefulness to S1 (Wright et al. 1995). Topographical studies by FFT Buchsbaum et al. (1982) studied sleep during daytime naps with 16 EEG channels. Delta power was highest in the midline. There were neither regional changes nor sleep state changes for theta activity. Alpha activity was greatest parieto-occipitally in restful waking with eyes closed. In slow wave sleep a frontal alpha dominance was observed. Topographic beta effects were minor. The data suggested that traditional sleep stages differ in the quantitative and topographic distribution of EEG frequency bands. Zeitlhofer et al. (1993) in their study on 10 young healthy volunteers without sleep complaints observed essentially similar results. Walsleben et al. (1993) studied whether topographic brain mapping could provide additional information for the detection of EEG changes associated with sleep apneas. They concentrated on S2, where theta and slow alpha activity frequently occurred in the frontal areas not clearly visible in the standard electrode sites used for RKS. The activity diminished during apnea. This diminution was related to the severity of the apnea. After the apnea the activity returned to baseline. Earlier Svanborg and Guilleminault (1990) had observed fast increases of delta activity during apnea in S1 and S2. This increase was either uniformly distributed or showed a post-central maximum. Just before the cessation of the apnea a strong increase in fast activity was found, indicating arousal. The rapid increase in delta activity may be caused by a very fast transition of vigilance into deeper stages of sleep. According to Wright et al. (1995) the greatest changes of EEG power occur in the posterior areas. Before sleep onset wakefulness alpha activity is located occipitally but alpha can also be seen in central regions. In drowsiness a more widespread topographic distribution over all the brain areas except in the temporal regions has been observed (Cantero et al. 1999). At sleep onset alpha power tends to decrease occipitally more than in other regions (Badia et al. 1994). In one of his early works Hori (1985) found that the alpha power decreased significantly 2 min. after onset of S1. The maximum difference in the disappearance of alpha activity between the different midline EEG derivations was approximately 15 s. After this a low-voltage pattern without a clear dominant peak was seen. At the onset of S1 the mean delta and theta power started to increase rapidly. The latency of increase in delta power was approximately 5 min. The increase in delta power was slower and slightest occipitally. Theta appeared first in Fz, Cz and Pz with a latency of approximately 3 min. and later in Fpz and Oz. Sigma activity also appeared earlier in Fz, Cz and Pz than in the frontopolar and occipital locations. The latency of appearance was 5 min. No significant regional differences in beta power were observed. Wright et al. (1995) also noted that theta increased from wakefulness to sleep in the midline sites. The greatest power change occurred in the 3-4 Hz band at the vertex. Lower frequency delta bands were not included in the study. 28

29 Tanaka et al. (1997) studied the topography of Hori s 9 stages. The maps showed the dominant areas of alpha activity to move along the midline of the scalp from the posterior areas to the anterior areas. The parieto-occipital alpha2 activity during wakefulness (stages 1 and 2) decreased markedly at stages 4 and 5, increasing in the frontal areas from stage 7. Sigma band activity did not show significant changes until stage 6, where a dominant sigma focus appeared in the parietal region. The sigma power increased sharply in stage 8. The dominant area of delta and theta band powers was first observed in the frontal region extending from the central to the temporal regions as a function of stage. The activities of delta -, theta -, and sigma bands did not show topographical changes but these activities developed in their focus areas ADAPTIVE SEGMENTATION Automatic adaptive segmentation EEG is not a stationary signal; its frequency and amplitude vary continuously. However, most quantitative methods used in EEG studies, including FFT, assume that the signal in the period analysed is stationary (Barlow 1985, Pardey et al. 1996a, Pardey et al. 1996b). The most common way to solve the problem of nonstationarity has been to use short, quasi-stationary segments. Usually the segments have been of equal length. Consequently the rapidly changing EEG signal cannot be completely stationary within the segments. In the FFT work presented above the fixed segment lengths have varied between 2.5 s and 60 s. With paper recordings it is a practical necessity to use long epochs of equal length for analysis. However, with digital visual or computer analysis the epoch boundaries could be placed freely. Barlow (1985) has stated that to study short nonstationarities a segment length of 1 s or less is necessary. The use of short segments is likewise not unproblematic. Analysis of S2 or SWS in 1 s epochs would cause artificial segmentation. For example, some segments would contain phasic events (spindles, KCs), whereas some would not. This was already pointed out by Dement and Kleitman (1957). In general, sleep stages consist of the combination of background activity and the phasic events. With a short segment these would be separated. In order to form a stage entity these would have to be combined to retrieve a meaningful result. On the other hand, use of short epoch does not remove the problem that the boundaries do not coincide with the changes in EEG activity. A second way to solve the problem of nonstationarities in the EEG is adaptive segmentation. In adaptive segmentation no fixed time period is used. Praetorius et al. (1977) were the first to apply automatic adaptive segmentation to EEG analysis. The system was originally developed to the analysis of clinical EEGs. The EEG stage boundary is indicated when there is a change in the EEG pattern. Usually a reference window and a moving test window are used. When a threshold based on the magnitude of the amplitude and frequency changes is exceeded, a segment boundary is created and a new segment is taken as the reference window. The procedure is then repeated. 29

30 Automatic adaptive segmentation procedure was found quite satisfactory (Creutzfeldt et al. 1985). The use of automatic adaptive EEG segmentation minimised human bias in the selection of portions of EEG recordings for automatic analysis (Barlow et al. 1981). In the work of Creutzfeldt et al. (1985) changes of vigilance were also well represented. Four clusters of different vigilance states were found: alpha, mixed-frequency (low voltage theta alternating), spindles and delta. The boundaries set by the computer program corresponded well to the visual estimation of epoch boundaries. In the earlier work of Barlow et al. (1981) only one channel was used for segmentation. In subsequent work it was stated that segmentation of even 8-12 channels could be feasible (Creutzfeldt et al. 1985). However, in order to avoid underclustering it was recommended that only 2 channels, the homologous pairs at each side be segmented simultaneously, after which different pairs be analysed separately. Another group which has applied adaptive segmentation of EEG in sleep research, was Gath and Bar-On (1985). The segments were classified into six clusters. In the opinion of these writers the division of the sleep EEG into segments of variable length took better account of the variations and continuity of the signal. In Tampere automatic adaptive segmentation has been applied in vigilance study. In a first application the EEG of multiple sleep latency test (MSLT) naps were automatically segmented (Hasan et al. 1993). One parieto-occipital EEG-lead was used for the segmentation procedure (P4-O2). As a result mainly short segments of s were obtained. More stages for wakefulness and drowsiness were used than in the standard scoring system and eye movements were also taken into account. The stages were: WM for artifacts and increased muscle and movement activity, WO for mixed-frequency EEG with fast eye movements, WC for posterior alpha rhythm with no eye movements, D for posterior alpha activity with SEMs or low-amplitude EEG with SEMs or no eye movements, S1 for increased theta activity and no fast eye movements, S2 for sigma activity and SREM for REM sleep periods. The stages WM, WO, WC correspond to wakefulness in the conventional scoring criteria. Stage D corresponds to wakefulness (alpha activity with SEMs) and partly to S1. S2 corresponds to S2, and SREM corresponds to SREM in the RKS rules. The segments were scored into stages both by computer analysis and visually. The method was further developed by applying it to the analysis of ambulatory recorded daytime polygraphic data (Hirvonen et al. 1997). As only one EEG channel was used for both segmentation procedure and for visual analysis, topographical aspects could not adequately be taken into account. The manmachine agreements were satisfactory (70 79 %) especially in high-alpha subjects. The agreements in low-alpha subjects were % (Hasan et al. 1993). The automatic segmentation procedure was never validated against visual adaptive segmentation. The same segmentation procedure with visual stage classification has later been used in two reaction time test studies with sleep apnea patients. The classification system with more stages and short segments gave a more accurate description of vigilance fluctuations than RKS (Conradt et al. 1999). In a subsequent work the stages were found to correlate with reaction time (Kinnari et al. 2000). 30

31 Visual adaptive segmentation To the best knowledge of the author the first attempt to visually divide sleep recordings into electrophysiologically homogenous segments is the work of Himanen et al. (1999). In that preliminary work of visual adaptive segmentation (VASS) the signal was both segmented and scored manually. Stages represented different frequencies and amplitudes and different kinds of eye movements were also taken into account. The stages were Wake-low for low-voltage mixed frequency EEG with fast eye movements or no eye movements. Wake-alpha was defined as posterior alpha and no eye movements. Drowsyalpha consisted of posterior or diffuse alpha activity with any kind of eye movements or frontal alpha activity with SEMs. Drowsy-low consisted of low-voltage mixed frequency EEG with SEMs. Theta activity had to be present in S1VASS and spindles or KCs in S2VASS. Arousals were scored separately as were periods with increased EMG activity or movements. Stages Wake-low, Wake-alpha and Drowsy-alpha correspond to wakefulness in the standard scoring system, and Drowsy-low and S1VASS correspond to S1. S2VASS corresponds to S2. In visual analysis it is easier to score on the basis of several channels than in automatic analysis. However, even if the aim was to take into account the topography of the EEG, it turned out that this division into stages did not separate electrophysiological stages into proper categories. It was also concluded that in future studies SEMs should be separated more distinctly from other eye movements MULTIPLE SLEEP LATENCY TEST IN VIGILANCE STUDY Definition of MSLT The MSLT is a daytime polygraphic study that is widely used in clinical practice to quantify daytime sleepiness (Carskadon 1993). It can be considered as the gold standard for the objective measurement of sleepiness because it is generally agreed to be valid and reliable (Johns 2000). There are also reference values for the assessment of normality. The MSLT consists of four or five tests performed at two-hour intervals. In the clinical form of the test patients are allowed to try to sleep for 20 min. If sleep occurs the test is continued for 15 min. to detect potential sleep onset REM periods. The maximum duration of the test is thus 35 min. Fast sleep onset parallels greater sleep tendency and slower sleep onset greater alertness. Sleep onset is defined as the first 30 s epoch scored as S1 (Carskadon 1986). The average sleep latency for the four or five latency tests is the most common parameter used to express the level of sleepiness. The average sleep latency of < 5 min. is generally considered pathological, indicating increased sleep propensity (Richardson et al. 1978, van den Hoed et al. 1981). Values 5 10 min. are considered borderline scores, and values > 10 min. are considered normal. 31

32 Problem of sleep onset Subjective perception of being asleep is of little value in determining sleep onset. It has been shown that the feeling of having slept corresponds poorly with psychophysiological measurements (see Pivik 1991). On the other hand it has been shown that subjective reports of having been asleep increase with deepening of sleep from S1 to S3+S4 (see Agnew and Webb 1972). Sleep onset can be defined by behavioral and EEG criteria. If an EEG criterion, for instance, MSLT latency, is used to define the onset of sleep, an adequate definition of sleep onset is needed. Since the tendency to sleep rather than the amount or maintenance of sleep is the variable of interest in the MSLT, the first epoch of S1 has been chosen as the marker of sleep onset. In addition the latency to S1 has been found to be associated with performance decrement (Carskadon and Dement 1979, Carskadon et al. 1981). Because sleep apnea patients have difficulties in staying asleep for more than s use of one S1 epoch is justified (Carskadon and Dement 1979). If the latency to the first S2 epoch was used the mean latencies of patients with narcolepsy became borderline and sleep apnea patients had almost normal values (Browman and Winslow 1989). Moment of sleep onset by EEG, subjective assessment, reaction times and hypnagogic imageries The moment of sleep onset was already investigated by Davis et al. (1937). With stimulus experiments the transition, subjective floating state, from wakefulness to real sleep was located in the B - state with low voltage EEG activity. This already took place after a 5 s interruption in the alpha rhythm. State C with spindles was considered definite real sleep. Gastaut and Broughton (1965) noted that hypnagogic imageries were most intense in stage 1B with loss of alpha activity. They were already present in stage 1A with slowing and diffusion of alpha rhythm and disappeared in phase 2. Hori and his group (Hori et al. 1994) using their 9-stage classification noted that most hypnagogic imageries were remembered in the theta stage (stage 5). These imageries were least frequent in stages 1 and 2 with alpha activity. When the 9 stages were grouped according to the subjective assessment of behavioral state, reaction time and recall rate of hypnagogic imageries the subgroups were not completely coincident with each other. This shows that the definition of sleep onset is dependent on the method of determination. As the proportion of subjective responses of having been asleep was only 43.7 % in conventional S2 the sleep onset period could be considered to extend beyond S1. If behavioral sleep is defined as lack of responses then S4 would be the only true sleep stage with no responses. On the other hand, response failures can also be seen during wakefulness. If the criterion for wakefulness is cognitive response to external stimulation, then only S3 and S4 and SREM can distinguish between true sleep and wakefulness, as cognitive responses are possible in S1 and S2. It seems that the presence or absence of the response is an uncertain measure of sleep onset. Slowing of the reaction time should be used as an additional parameter (Ogilvie and Wilkinson 1988). 32

33 S1 is a stage which is neither simple wakefulness nor sleep. In terms of behaviorally defined sleep and wakefulness both have been found to be almost equally likely to be present in S1 (Johnson 1973, Ogilvie and Wilkinson 1988). On the other hand, it has been shown by reaction time tests that S1 differs from wakefulness in response rate and latency (Ogilvie et al. 1989). S1 can be seen as a transition period with fluctuating nature between wakefulness and sleep. Slower and less frequent reaction time test response has been obtained in S2 than in S1 (Ogilvie et al. 1989). S2 is in general thought to represent true sleep (Agnew and Webb 1972, Johnson 1973, Webb 1986). Ogilvie has suggested that the transition from wakefulness to sleep should be dealt with as a sleep onset period instead of dividing it into discrete stages (Ogilvie et al. 1989). SEMs at sleep onset Although the EEG can be considered an important indicator of vigilance, eye movements, especially SEMs, are also significant. This was pointed out already by Miles (1929). Rechtschaffen and Foulkes (1965) conducted an experiment where they kept the eyes of the sleeping subjects open. When objects were presented during alpha with SEMs there was no recall of the objects. Hori (1982) observed that SEMs are concomitant with a process preceding or initiating drowsiness and that they disappear once drowsiness has reached a certain level. The same phenomenon was observed by Ogilvie et al. (1988), who studied reaction times and SEMs. They noticed that SEMs were absent when response rate was fast, with moderate response rates SEMs were more prevalent, diminishing with very slow responses. In S2 and with behavioral sleep determined by response failures SEMs were absent. On the other hand SEMs have been observed in wakefulness with closed eyes (Shimazono et al. 1965). Summarising the definition of sleep onset As sleep onset is difficult to define in terms of a single parameter or stage the use of several parameters gives more confidence. Many studies have been performed by comparing only one electrophysiological and one behavioral or performance parameter or two electrophysiological parameters. It would be more conclusive to use a combination of EEG, SEMs, muscle tonus and performance with additional autonomic nervous system measurements such as the respiration pattern which is unstable at sleep onset and stabilises in S2 (Ogilvie and Wilkinson 1984, Ogilvie et al. 1988). If EEG, EMG and eye movements (EOG) explain what sleep is, they should correlate with behavioral definitions of sleep. Relevant behavioral changes at sleep onset are, however, not synchronised. It is clear that it is impossible to define an unambiguous moment of sleep by any combination of parameters that would suit all needs and conditions. The temporal dispersion in the signs of falling asleep makes the task even more difficult. Therefore, as Rechtschaffen (1994) has emphasised, one should focus on the purpose and utilisation of the definition. If the aim is to track the first moments of vigilance impairment then very early signs of drowsiness, for instance, changes in the alpha activity and slowing 33

34 of eye movements should be considered. If, on the other hand, one is interested in knowing when the subject is definitely asleep then the disappearance of responses and the appearances of sleep spindles should be noted VALUE OF MSLT IN DETERMINING SLEEPINESS Positive findings Conflicting results have often been obtained when correlations between MSLT scores and subjective sleepiness or performance impairment have been studied. Sleep deprivation studies have shown clear correlations between MSLT and the duration of deprivation, subjective sleepiness or performance (Carskadon and Dement 1981, Carskadon et al. 1981, Carskadon and Dement 1979, Borbély et al. 1985). However, it was later concluded that only very low MSLT scores had a relationship to performance (Carskadon and Dement 1982). In clinical work narcoleptic patients can in general be distinguished from patients with other sleep disturbances and normal controls by the MSLT (Richardson et al. 1978, van den Hoed et al. 1981). In patients with suspected excessive daytime somnolence (EDS), subjective sleepiness measured by Epworth Sleepiness Scale (ESS) correlated negatively, but not strongly (correlation coefficient 0.37), with MSLT scores. ESS scores of 14 and above (range 0 24) predicted a low mean sleep latency on the MSLT (Chervin et al. 1997) Negative findings Subjective sleepiness or difficulties in falling asleep do not necessarily correlate with the MSLT latency (Chervin et al. 1995). Mean sleep latencies between 5 and 10 min. and sometimes even lower scores can be found in subjects without sleep complaints (Roth et al. 1980, Manni et al. 1991, Harrison and Horne 1996a, Geisler et al. 1998). Carskadon and Dement (1979) found a baseline mean sleep onset latency of 5.4 min. in healthy students. In a study on a patient population with sleep apnea, periodic limb movement syndrome (PLMS) and complaints of insufficient sleep, no correlations between MSLT scores and subjective sleepiness were obtained (Pressman and Fry 1989). In insomniacs and normal controls no significant relationships between subjective sleepiness and MSLT scores were found (Seidel et al. 1984). No correlations were found between MSLT and personal inventory scores or tension and anxiety. In the control group slower card sorting was associated with shorter MSLT latencies. 34

35 In the extensive study by Johnson et al. (1990) no correlations between MSLT and performance or mood ratings in normal subjects were obtained. In a subsequent study MSLT and subjective sleepiness were significantly correlated at 06:00h, but the correlation became nonsignificant as the day progressed (Johnson et al. 1991). Correlations between lapses in tapping-task and subjective sleepiness were generally nonsignificant. MSLT and tapping-task were correlated in the group as a whole but not in all subgroups. According to the study a 5 min. mean MSLT latency did not necessarily indicate EDS but could also be a sign of a good sleeper. Relatively short MSLT latencies have been obtained with normal scores in a psychological task sensitive to sleepiness (Harrison and Horne 1996a). In a study on elderly subjects MSLT-defined alertness/sleepiness was unrelated to neuropsychological test results (Bliwise et al. 1991). In a recent study the sensitivity and specificity of ESS, MSLT and maintenance of wakefulness test (MWT) were compared (Johns 2000). MSLT was found to be the least discriminating test of daytime sleepiness between narcoleptic and normal subjects. The use of MSLT as a gold standard was strongly criticised MSLT and nocturnal polygraphic parameters Variable results have been obtained when parameters derived from night polygraphies (PSG) have been correlated with MSLT scores. In a clinical population study the only nocturnal parameter having a significant correlation with the MSLT score was sleep onset latency of the PSG (Chervin et al. 1995). However, the correlation coefficient even for this variable was only The correlation coefficients for demographic or other PSG variables including TIB, SEI, TST, minutes and percentages of sleep stages, wake after sleep onset, number of awakenings, respiratory disturbance index and lowest SaO 2 were between and In a study on clinical patients MSLT latency had positive correlations with short PSG latency and the amount of stages 3-4 (Guilleminault et al. 1988). There was also a negative correlation between the MSLT score and the amount of nocturnal S1. Roehrs et al. (1989) studied correlations between MSLT latency and respiratory parameters and arousals. The correlation coefficients found were all between 0.33 and Van den Hoed et al. (1981) studied EDS patients suffering from various sleep disorders having short (< 5 min.), moderately long (5-11 min.) and long (> 11 min.) MSLT latencies. In general patients with short MSLT latencies had short sleep latencies at night, shorter sleep cycles, higher sleep efficiency and earlier REM sleep than the patients with long MSLT latencies. Stepanski et al. (1984) found a 0.48 correlation coefficient between the total number of arousals and sleepiness in MSLT. However, in closer analysis of different subject groups (apnea, PLMS, normals) the correlation coefficients remained under Insomnia patients showed a negative correlation (-0.50) between MSLT defined sleepiness and arousals. 35

36 Experimental nocturnal sleep fragmentation has been shown to cause a statistically significant reduction in the MSLT latency on the following day (Roehrs et al. 1994). Latencies changed from normal to almost borderline scores. The dispersion was, however, considerable. In the same year, Philip et al. (1994) obtained similar results MSLT in sleep apnea syndrome Obstructive sleep apneas consist of repetitive cessations of airflow caused by obstruction in the upper airways. The obstructive sleep apnea syndrome (OSAS) consists of both polygraphic and clinical findings. The diagnostic criteria proposed by the International Classification of Sleep Disorders are presented in Table 1. Apneas and hypopneas cause sleep fragmentation (Isono and Remmers 1994). A principal consequence of OSAS is excessive daytime sleepiness (Guilleminault 1994). OSAS and snoring have potential medical complications such as arterial hypertension, heart disease and brain infarction (Partinen 1994). Table 1. Diagnostic critria for the obstructive sleep apnea syndrome according to ICSD (1997) A. The patient has a complaint of excessive sleepiness or insomnia. Occasionally, the patient may be unaware of clinical features that are observed by others. B. Frequent episodes of obstructed breathing during sleep. C. Associated features include: 1. Loud snoring 2. Morning headaches 3. A dry mouth upon awakening 4. Chest retraction during sleep in young children. D. Polysomnographic monitoring demonstrates: 1. More than five obstructive apneas, greater than 10 seconds in duration, per hour of sleep and one or more of the following: a. Frequent arousals from sleep associated with the apneas b. Bradytachycardia c. Arterial oxygen desaturation in association with the apneic episodes 2. MSLT may or may not demonstrate a mean sleep latency of less than 10 minutes. E. The symptoms can be associated with other medical disorders (e.g. tonsillar enlargement). F. Other sleep disorders can be present (e.g. periodic limb movement disorder or narcolepsy). Minimal criteria A + B + C. Reproduced with permission of the American Academy of Sleep Medicine. 36

37 The MSLT findings in sleep apnea patients are variable and in clinical studies the patients and controls do not necessarily differ from each other. In sleep apnea patients MSLT mean latency is often, but not always, shortened (Guilleminault et al. 1988). On the other hand short sleep latencies have been obtained in patients with low apnea indices and just slightly disrupted sleep architecture (Valencia-Flores et al. 1993). As with other patient groups or healthy subjects subjective sleepiness of apnea patients does not necessarily predict the outcome of the MSLT (Dement et al. 1978). Attention has also been paid to lack of differences in subjective sleepiness between OSAS patients and controls (Roth et al. 1980). Chervin and Aldrich (1998) used regression analysis to study the relationships between MSLT and respiratory parameters in PSG in a population of 1046 patients. Several parameters were significantly associated with sleepiness measured by MSLT. The supine apnea-hypopnea index (AHI) predicted the MSLT results better than the total AHI. The rate of obstructive apneas was also more relevant to excessive daytime sleepiness than the rates of other types of respiratory events. In any case, in another study no relationships between respiratory variables and the MSLT scores were found (Roth et al. 1980). In a pooled data set with apnea patients and controls MSLT correlated moderately negatively with nocturnal %S1 and shifts to S1. In the controls a positive relationship between MSLT and the amount of wakefulness and a negative correlation between MSLT and shifts to S1 were obtained. No correlations between MSLT score and sleep parameters were found in the patient group. It was concluded that daytime sleepiness is related to nocturnal sleep disruption in normal subjects but not in patients Evaluation of MSLT as an indicator of sleepiness It is generally accepted that the MSLT measures the tendency to sleep in a sleep-inducing environment without disturbing factors (Carskadon 1986). Although subjective estimation, performance tests and the MSLT scores give different results the MSLT is still regarded as the most valid measure of sleepiness. MSLT is considered to be less influenced by confounding factors such as muscle fatigue, motivation and practice than are performance tests. In addition to evaluating sleepiness, it makes it possible to check for sleep onset REM periods. (Richardson et al. 1978, Carskadon and Dement 1979). In the opinion of Matousek and Petersén (1983) the poor definitions of subjective feelings of vigilance makes subjective assessment of vigilance unreliable especially when different individuals or different situations are compared. Reaction time studies are not satisfactory because the measurements influence vigilance. These factors do not affect the EEG recordings. Subjective estimates of sleepiness may become unreliable if the subject has been adapted to sleepiness for a very long time. In sleep apnea patients the progression of EDS may be so slow that the patient becomes accustomed to it and no longer perceives sleepiness. Sleepiness can also be denied if there is a fear of being labeled lazy or unmotivated (Dement et al. 1978). 37

38 It has been stated that one night of poor sleep does not affect the MSLT result. The shortening of MSLT latency would not be visible until after two nights of partial (5 h) restriction (Carskadon and Dement 1981). On the other hand one night of sleep fragmentation has been found to cause a change in the MSLT score without an impairment of performance (Philip et al. 1994). It has been suggested that in sleep apnea patients the MSLT is more sensitive and therefore gives different results than subjective estimation. It has also been postulated that the MSLT is independent of motivational factors and the need to deny sleepiness (Roth et al. 1980). Dement et al. (1978) also claimed that performance testing can be confounded by motivational factors and therefore the MSLT would be preferable. However, it has been argued that motivational factors can also play a role in the MSLT. Broughton (1994) has emphasised the role of compliance. In one study subjects who were rewarded by economic incentives managed to stay awake longer than those who did not receive any reward, even if subjective estimates of sleepiness and performance were the same (Alexander et al. 1991). In another study, subjects fell asleep more quickly when they were economically rewarded. The effect was, however, limited only to the afternoon nap (Harrison et al. 1996). Other than motivational factors may also influence the MSLT result. It has been shown that even modest physical activity like 5 min. of walking prior to the nap as compared to watching television can prolong the MSLT latency by approximately 6 min. (Bonnet and Arand 1998). Walking also abolished the afternoon dip in MSLT latencies. Sleepiness is not necessarily a single entity (Broughton 1982). MSLT can be effective in differentiating between pathological and normal sleepiness but it may not be appropriate in measuring subtle variations in sleepiness (Roth et al. 1980). In summary, MSLT may measure the intensity of sleepiness but it is not useful in evaluating the quality or the etiology of sleepiness. In clinical practice MSLT seems to be useful in determining sleepiness related to narcolepsy. It can also be used in experimental studies. Otherwise the MSLT is not very sensitive or specific. It is not clear whether the reason is the test protocol itself or the way of scoring and the parameters used Increasing the sensitivity of MSLTs Scoring with epochs is not necessarily a suitable method for analysing MSLTs. Due to the fluctuating nature of waking and sleep at sleep onset it is somewhat accidental which epoch is the first to be scored S1. Attempts have been made to increase the sensitivity of the MSLT by using shorter epochs or detection of sleep episodes of shorter duration. Determination of sleep latencies by scoring microsleep episodes has been applied by several groups. Pressman and Fry (1989) defined sleep onset as the first 10 s of continuous sleep because of sleep fragmentation due to apneas and hypopneas. Unfortunately they did not score sleep onset also to the first S1-epoch for comparison. Therefore the effect of microsleep scoring remains unclear. Harrison and Horne (1996b) obtained shorter MSLT sleep latencies and more sleep onsets by using a 5 s continuous sleep episode than by using 38

39 conventional single-epoch sleep onset criteria. Valencia-Flores et al. (1995) compared 5, 10 and 30 s epochs in a 1-nap study. The percentage of subjects that could be classified as severely sleepy was greater by the 5 s epoch criteria with respect to the standard 30 s epoch duration. Particularly in sleep apnea patients it is assumed that short microsleeps reveal loss of vigilance more precisely than the conventionally scored MSLT mean latency. Obstructive apneas and the related arousals are known to prevent or delay sleep onset in 4.8% to 28.6% of MSLT naps when using a single epoch of S1 criteria (Browman and Winslow 1989). Due to this many sleep laboratories have started to correct sleep onset latency of the MSLT to the first respiratory event, even if it is not proposed in the MSLT guidelines. As far as is known no additional studies about the effects of this method on patients and normals have been conducted. By adaptive segmentation the short vigilance changes could be detected (Hasan et al. 1993, Himanen et al. 1999). Therefore adaptive scoring would be particularly suitable for vigilance study and OSAS patients. Quantitative methods have also been applied to MSLT demonstrating spectral differences within the sleep onset period of narcoleptics and normal sleepers. (Alloway et al. 1999). Mean delta power was found to be higher in narcoleptic naps containing REM sleep or S2 as compared to naps with S1 or S2 of control subjects. As the homeostatic process can be exposed by slow wave activity, the use of quantitative methods in MSLT diagnostics and in the study of sleep onset dynamics was suggested to supplement visual judgement. 39

40 3. PURPOSE OF THE STUDY The aims of the present study were: 1. To develop visual scoring of sleep recordings: to improve the temporal resolution of the conventional scoring method. To reveal minor vigilance state fluctuations by defining new vigilance stages. 2. To examine whether multiple sleep latency test parameters obtained by the new scoring method would be better in differentiating between sleepy patients and healthy subjects than the parameters derived from the conventional scoring method. 3. To find out whether the new scoring method would be suitable for the study of sleep onset and sleep dynamics. 4. To study if the new scoring method would provide a better basis for automatic sleep analysis than the conventional scoring method. 5. To examine whether the new vigilance stages defined purely by the morphology present spectral differences specific to different vigilance states. 40

41 4. SUBJECTS AND METHODS 4.1. SUBJECTS Most recordings were part of a larger project (Siesta). The patients were recruited through newspaper advertisements or were referred by their physicians. The control subjects were recruited through advertisements. Healthy volunteers had to be free from any sleep complaints and they must not have had subjective EDS. At the beginning the author interviewed all the patients and control subjects and performed a general physical examination to exclude any primary medical or psychiatric disorder. None of the subjects used hypnotics or other medication affecting the central nervous system. The inclusion criteria in the study for the patients were a clinical picture and subjective complaints of OSAS according to the International Classification of Sleep Disorders (ICSD 1997, Table 1) and an AHI >10/h in the preceding polygraphic whole night recording. Exclusion criteria for control subjects were Mini-Mental State Examination score (MMSE, Folstein et al. 1975) < 25, Pittsburgh Sleep Quality Index (PSQI, Buysse et al. 1989) > 5, habitual bedtime before or after 24.00, Zung Anxiety Scale (Self-rating anxiety scale, SAS, Zung 1971) raw score > 33 and Zung Depression Scale (Self-rating depression scale, SDS, Zung 1965) raw score > 35. Control subjects had to have an AHI of < 10/h. Six men and four women of all the OSAS patients studied met the inclusion criteria. They represented typical sleep apnea patients suffering from mild to severe disorder. Altogether three men and four women were accepted as control subjects. All subjects gave their written consent to participation in the study. Subjects were not paid for participating. The median ages of the 10 patients and the 7 controls in the first part of the present study were 54 years (range 35-63) and 49 years (25-77) respectively. One female patient did not participate in psychological tests, vigilance test or reaction time test and was not recruited for the psychometric part of study. In this part of the study the median age for the patient group was 53 years (range 35-63). All patients and control subjects with MSLTs without major technical failures were included in the spectral part of the study. Since only one control subject presented low voltage alpha activity his recordings were not included in the pooled data. This left six patients and four control subjects out of total 17 subjects and one control subject with poor alpha activity. Three patients and three control subjects were female. From one female patient only three naps were studied by FFT due to technical difficulties. In this part of the study the subjects ranged in age from 43 to 63. The control subject with poor alpha activity was 25 years old. 41

42 4.2. RECORDINGS The subjects underwent an investigation consisting of a whole night polygraphy together with psychological tests, reaction time test, vigilance test and an MSLT the following day. All studies were performed at the sleep laboratory of the Department of Clinical Neurophysiology, Tampere University Hospital. The recordings were performed in a sound-attenuated laboratory room in a controlled environment with a temperature of about 22 o C. The subjects attended to the laboratory at 7 p.m. and they retired to bed between 10 and 12 p.m. depending on their habitual bedtimes. They were allowed to sleep for a maximum of 8 hours. The MSLTs were started after psychometric tests, not until two hours from awakening. The four naps were recorded at approximately 10:00, 12:00, 14:00 and 16:00. The standard clinical guidelines for MSLT were followed (Carskadon 1986). The MSLT naps were terminated 20 min. after lights out if there was no sleep at all. If the subject fell asleep, the recording was continued for 15 minutes from the first sleep stage 1 (S1) epoch. The subjects were instructed not to sleep between naps. The daytime and night-time recording montages were similar. Seven EEG derivations Fp1- M2, C3-M2, O1-M2, Fp2-M1, C4-M1, O2-M1, M2-M1, two EOG-channels EOG P8-M1, EOG P18-M1 (Häkkinen et al. 1993) and submental muscle tonus were recorded. In addition the following parameters were measured: m. tibialis anterior muscle tonus by surface electrodes, body position, electrocardiogram, oro-nasal airflow by thermistor, thoracoabdominal respiratory movements by piezo transducers and blood oxygen saturation by oximetry. A 16-channel Embla sleep recorder (Flaga) with a dynamic range of 16 bits and a sampling rate of 200 Hz was utilised. This gave a bandwidth of approximately 90 Hz by using a completely digital flat filter. The direct recording mode was used so that data was sampled directly on the hard disk of a personal computer. This allowed on-line monitoring of the signals in order to secure the quality of the recordings PSYCHOMETRIC TESTS All subjects completed two psychological self-rated scale tests in advance. These were Quality of Life Questionnaire (QOL) and Epworth Sleepiness Scale (Johns 1991) which was slightly modified to suit the Finnish conditions better (mess). All psychometric tests were scored and supervised by undergraduate psychologists. The psychometric tests were Adjective Mood Scale for subjective well-being (Bf-S, von Zerssen et al. 1970), Alphabetical Cancellation Test, an alphabetic cross-out test for attention and concentration (AD-test, Gruenberger 1977, pp ), Gruenberger Fine- Motor-Test for psychomotor activity (FM-test, Gruenberger 1977, pp ) and Digit span test for numerical memory. These were carried out in the morning after getting 42

43 dressed and having breakfast (with habitual coffee and cigarettes, no alcohol) between 1 and 2 hours after getting up. The Vienna Reaction Time Test for Screen (Developed by Dr. G. Schuhfried) was used to measure reaction time to visual and acoustic stimuli. Test duration was approximately 5 minutes. In this test two coloured circles were represented on the screen of the personal computer. A concomitant sound stimulus was given with some of the visual stimuli. When the left circle was yellow with the simultaneous acoustic stimulus the subject had to move the finger from one button to another. The mean reaction time (RT-test) and number of misses (RT-miss) were calculated for each subject. A longer version of the Quatember-Maly vigilance test (Vigil, developed by Dr. G. Schuhfried) was used to measure vigilance in a low-stimulus situation. Test duration was 45 minutes. In this test a bright dot moved along a circular path on a PC screen. If the dot jumped twice the usual distance, the subject had to react by pressing the reaction button. The mean value of reaction times was calculated (Vigil) for each subject. The difference between the mean reaction times of the last and first thirds of the test session was also calculated (VIGdiff) VISUAL SCORING Night recordings The night recordings were scored into sleep stages by the standard method of Rechtschaffen and Kales in epochs of 30 s (RKS, 1968). The following parameters were measured. Time in bed (TIB) is the time from the beginning of the recording to the end of the recording in minutes. Sleep onset latency (SOL) is the interval from lights off to the first S1 epoch, which is followed by at least two consecutive sleep epochs or to the first epoch of any other sleep stage, provided that it appears before the three consecutive S1 epochs. Total sleep time (TST) is the time spent asleep from sleep onset to the end of the final sleep epoch. Sleep efficiency index (SEI) is the percentage of TST from TIB. Percentages of total times of stages S1, S2, S3, S4 and SREM referred to TST were also calculated. Awakening is defined as at least one 30 s epoch containing more than 50% of wakefulness. Awakening index (AwakeI) is the number of awakenings and epochs scored as movement time (MT) per hour referred to TST. The Stage shift index (ShInd) is the number of stage shifts per hour referred to TST. Microarousals were scored according to guidelines suggested by the atlas task force of the American Sleep Disorders Association (ASDA 1992). Arousal index (ASDARI) is the number of these microarousals per hour. Microarousals were also scored according to the arousal criteria of visual adaptive scoring of sleep (VASS) with the exception of minimum duration of 2 s and a demand for 10 s of preceding sleep. The VASS criteria for arousals are presented in Table 2. The definitions of the arousals 43

44 Table 2. Characteristics of stages in VASS Stage Abbreviation Electrophysiological characteristics Wake-low WL Low-voltage mixed-frequency EEG with fast eye movements or no eye movements. Wake-alpha WA Posterior alpha in EEG with fast eye movements or no eye movements. Wake-alpha-F WAF Diffuse or frontocentral alpha in EEG with fast eye movements or no eye movements. Alpha-SEM SA Posterior alpha in EEG with SEMs. Alpha-SEM-F SAF Diffuse or frontocentral alpha in EEG with SEMs. Drowsy-low DL Low-voltage mixed-frequency EEG with SEMs. S1 S1VASS EEG with increased theta activity. S2 S2VASS Spindles and/or K complexes in EEG. REM REMVASS Theta activity, saw tooth waves in EEG with REMs and low muscle tonus. Arousals In S1VASS and REMVASS additional EMG-increase or cable artifact is required. Alpha-arousal (1, 2) Aa (1,2) Appearance of alpha activity in EEG. From stages S1VASS and S2VASS, respectively. K-alpha-arousal (2) A-Ka (2) K-complex with alpha activity in EEG. From S2VASS. Delta-arousal (2) A-delta (2) Abrupt hypersynchronous slow-waves in EEG with EMG augmentation. From S2VASS. EEG attenuation (1, 2) A-desyn (1,2) Phases with desynchronized EEG-patterns. From S1VASS and S2VASS, respectively. EMG-activity in wake EMGW Elevated muscle activity in wakefulness stages and in SA + SAF. EMG-activity in sleep EMGS Elevated muscle activity in DL and in sleep stages. Movement in wake MTW Muscle activity with cable artifacts in wakefulness stages and in SA + SAF. Movement in sleep MTS Muscle activity with cable artifacts in DL and in sleep stages. SEM, slow eye movement; REM, rapid eye movement; EMG, submental muscle tonus.

45 roughly follow the definitions of CAP phase A of Terzano et al. (1996) and definite arousals by Evans (1993). The index of these modified microarousals (mari) is also referred to TST. The Apnea-hypopnea index AHI is calculated as the hourly rate of cessations or diminutions > 50% of airflow lasting over 10 s. ODI 4 is the number of decreases in SaO 2 of at least 4 %. SaO 2 minimum (SaO 2 min) is the lowest value of oxygen saturation in sleep MSLT scoring All MSLTs were scored both by conventional sleep stage scoring (RKS) and by visual adaptive scoring (VASS). RKS was based on the EEG derivation C4-M1. A 30 s epoch was used as stated in the guidelines (Carskadon 1986). In VASS the changes of the electrophysiological characteristics of the signal are used to determine the segment boundaries instead of a fixed epoch (Figure 2). Electrophysiological stage changes shorter than 1 s are not scored separately, which is in line with the recommendation of the Comac BME task force (Kemp 1993). The information from the frontal and occipital EEG derivations is also taken into account by separating the occipital alpha activity from the more diffuse alpha activity. KCs, which are sometimes identifiable only in frontal leads, are also used in scoring as well as frontal spindles. Figure s of polygraphic tracing. Channels from top to bottom: EEG Fp1-M2, C3-M2, O1-M2, Fp2-M1, C4-M1, O1-M2 and M2-M1. EOGdx-M1, EOGsin-M1, submental EMG tonus. RKS scoring is shown on trace 4, VASS on trace 5. At the beginning there is low voltage activity with SEMs (Drowsy-low, DL), which is followed by S1VASS. Somewhat later occipital alpha activity with SEMs (Alpha-SEM, SA) appears. This was not scored as an arousal since no changes occurred in submental EMG-channel. S1VASS gives way to occipital alpha activity with eye movements but not SEMs (Wake-alpha, WA). Next a short segment of Wake-low (WL) was scored (lowamplitude activity, no SEMs). At the end a short Alpha-SEM (SA) segment is followed by S1VASS. 45

46 The VASS stages with their descriptions are shown in Table 2. The stages were chosen so that morphologically different states could be separated. VASS stages Wake-low (WL), Wake-alpha (WA), Wake-alpha-frontalis (WAF), Alpha-SEM (SA) and Alpha-SEM-frontalis (SAF) correspond to S0 in RKS (S0RKS). Drowsy-low (DL) is defined by attenuation of alpha activity and presence of low-voltage EEG activity combined with SEMs. Scoring of S1 in VASS (S1VASS) requires the presence of theta activity. Both of these stages correspond to S1 in RKS (S1RKS). The stages were separated because they are assumed to reflect different levels of vigilance (Hasan et al. 1993). In some of the calculations DL and S1VASS were combined in order to make RKS and VASS more comparable. S2VASS is identical to S2RKS in this study, which concentrates on the evaluation of short daytime recordings. The appearance of the first well formed spindle or KC is used as the starting point of S2VASS. Arousals are scored as described above with a minimum duration of 1 s. One arousal may consist of several different VASS stages (Figure 3). When the number of arousals was calculated these several stages forming just one arousal were combined. SEMs are defined as slow eye movements with a rise time > 0.5 s and a duration > 1 s. Figure s of polygraphic recording, channels as in Figure 2. At the beginning of the tracing the EEG has been scored as S2 by both methods. S2RKS is followed by wakefulness (trace 4). On trace 5 the VASS scoring demonstrates arousal with KC and alpha activity (arousal-k-alpha), which is followed by EMG augmentation in sleep (EMGS) and alpha activity with SEMs (Alpha-SEM, SA). 46

47 The VASS stages are modified from the preliminary pilot VASS study (Himanen et al. 1999) where topographical differences of alpha activity could not be properly taken into account. In the present study alpha activity with SEMs is also separated from the alpha activity with other eye movements or blinks because alpha activity with SEMs is specifically assumed to reflect impairment of vigilance (Kojima et al. 1981, Santamaria and Chiappa 1987). Two new stages (WAF and SAF) were defined. To examine in which stages the arousals occurred arousals are also coded by stage. Scorings were done by using the Somnologica program version 1.6 (Flaga). For VASS a special tool made by the aid of the developer s toolkit provided by the manufacturer was utilised. The PC computer used for analysis had a 21 screen with a 1280 x 1024 resolution. Repeatability of VASS To examine the repeatability of the new scoring method the author re-scored 8 naps blindly. The time interval between scorings was approximately one year. Five of the naps were from apnea patients and three naps from control subjects. The percentage of time with identical scorings was calculated for each nap separately (see Figure 4). Figure 4. Comparison between two independent scorings. At the beginning of the tracing both scorings show S2VASS. The starting point for Arousal-Ka2 differs slightly as does the ending time of the arousal. In the upper scoring DL, which is followed by S1VASS, is shown, whereas in the lower scoring S1VASS is scored immediately after the arousal. The black bars below mark the time when scorings are identical. 47