Sleep-disordered breathing is a widespread disease 1. Breath-to-Breath Variability Correlates With Apnea-Hypopnea Index in Obstructive Sleep Apnea*

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1 Breath-to-Breath Variability Correlates With Apnea-Hypopnea Index in Obstructive Sleep Apnea* Peter Kowallik, MD; Ilka Jacobi, MD; Alexander Jirmann, MD; Malte Meesmann, MD; Michael Schmidt, MD; and Hubert Wirtz, MD Background: Breathing in patients with obstructive sleep apnea (OSA) is frequently interrupted by periods of hypopnea and apnea. There is limited information regarding a possible disturbance of breathing outside these periods. Study objective: To analyze the degree of breathing disturbance during nonocclusion. Design: Prospective determination of breathing variability during full polysomnographic sleep studies. Patients: Breath-to-breath variation was monitored in 34 patients with OSA and in 9 healthy subjects. Measurements and results: All breath-to-breath intervals were automatically analyzed from flow signal, displayed, and manually corrected for artifacts. Distribution of all nonapneic breath intervals was analyzed for the extent of difference from a normal distribution pattern by specifying kurtosis. In untreated OSA patients, kurtosis was significantly reduced ( , mean SD) compared to control subjects ( ), indicating increased variability of nonoccluded breathing. This effect was present in all sleep stages, and the extent depended significantly on the degree of disease. Continuous positive airway pressure breathing was able to normalize kurtosis ( ) immediately. Conclusions: Breathing in OSA is not only characterized by interruptions of breathing during occlusion, but by a greater variation in the pattern of normal-length breaths. (CHEST 2001; 119: ) Key words: airflow obstruction; breathing variability; continuous positive airway pressure; obstructive sleep apnea Abbreviations: AHI apnea-hypopnea index; AI apnea index; CPAP continuous positive airway pressure; OSA obstructive sleep apnea; REM rapid eye movement; UARS upper-airway resistance syndrome *From the Department of Medicine (Drs. Kowallik, Jacobi, Jirmann, Meesmann, and Schmidt), University of Würzburg, Würzburg; and the Department of Medicine (Dr. Wirtz), University of Leipzig, Leipzig, Germany. Manuscript received January 12, 2000; revision accepted September 6, Correspondence to: Peter Kowallik, MD, Medizinische Universitätsklinik, Josef-Schneider-Str. 2, D Würzburg, Germany; kowallik@mail.uni-wuerzburg.de Sleep-disordered breathing is a widespread disease 1 with varying forms of manifestation. 2 The most severe form is obstructive sleep apnea (OSA), with daytime sleepiness and associated cardiovascular disease and other sequelae. 3 5 The underlying condition is increased upper-airway collapsibility for reasons yet to be determined. 6 Even if upper-airway obstruction is incomplete, increased upper-airway resistance will still cause clinical symptoms similar to OSA 7 because of respiratory effort-related arousals. 8 Diagnosis of OSA involves screening as well as respiratory nocturnal polysomnography. 9 For detection of upper-airway resistance syndrome (UARS), measurement of esophageal pressure in combination with arousal detection is the gold standard. 10 Both of these diagnostic procedures are time-consuming and require considerable technical expense, while causing patient discomfort. In general, an increase in resistance of the upper airway will lead to changes in the flow contour 11 and lengths of the breathing cycle, as well as increasing the variability of breath-cycle length. 12,13 Thus, sleep-disordered breathing with the common underlying condition of increased upper-airway resistance may be characterized by a varying degree of breath cycle-length variability different from the normal pattern of breathing. The purpose of this study was to verify whether or not breath cycle-length variability, readily determined by standard thermistor flow-sensor equipment, is significantly different in OSA patients compared to healthy control subjects, and whether or not it correlates with the extent of OSA estimated by apnea-hypopnea index (AHI). CHEST / 119 / 2/ FEBRUARY,

2 Study Population Materials and Methods Thirty-four patients (age range, 30 to 68 years) were referred to this institution for polysomnographic evaluation because of daytime sleepiness or other sleep apnea-related symptoms (Table 1). Nine healthy volunteers (age range, 22 to 52 years) without a history of daytime sleepiness, and having normal findings on physical examination and functional tests, such as resting ECG, echocardiography, lung function assessed by body plethysmography, and home sleep study (Jäger Apnoe Screen 2 Plus; Jäger- Tönnies; Würzburg, Germany), served as a control group. Control subjects gave informed consent to participate in the study. Study Protocol Patients as well as healthy subjects underwent a full-night polysomnographic sleep study. Patients in whom OSA was diagnosed and who received continuous positive airway pressure (CPAP) treatment underwent a second polysomnographic study the next night. Polysomnography Polysomnography was performed for 8 h during the night (Sleep Lab 1000 P; Jäger-Tönnies). Fourteen channels were recorded continuously: airflow signal (32-Hz sampling rate) recorded by a nasal/oral thermistor flow sensor (Jäger-Tönnies), two EEG channels, two electromyogram channels, two electrooculogram channels, one channel recording limb activity, one channel recording body position, two channels recording thoracic and abdominal effort, one channel recording snoring, one channel recording transdermal oxygen saturation, and one ECG channel. Therapy Patients with an AHI of 30, or an AHI 5 and a history of symptoms typical for sleep apnea, received treatment with CPAP (Somnotron 2; Weinman; Hamburg, Germany) if they consented to this kind of treatment, as recently recommended in a consensus statement. 14 A nasal CPAP mask was fitted, and the effective pressure individually determined for each of these seven patients. Starting from a pressure of 5 millibars (5.1 cm H 2 O), the CPAP pressure was increased by 1 millibar (1.02 cm H 2 O) whenever there was more than one significant fall in oxygen saturation, apnea, or hypopnea. 14 When this did not occur for 1 h, pressure was slowly decreased until desaturations reappeared, in which case pressure was increased again. Data Analysis Polysomnographic Sleep Study: Evaluation of the sleep studies were done according to guidelines. 15,16 Sleep stage was defined according to standard criteria 15 and grouped into four different levels: awake, rapid eye movement (REM), sleep stages 1 and 2, and sleep stages 3 and 4. Hypopnea was determined as a reduction in the flow signal of 50%, lasting 10 s and causing a decrease in the oxygen saturation of 4%. 2,17,18 Apnea was defined as an interruption of the flow signal for a duration of at least 10 s. 2,17 The number of apneas and hypopneas were calculated to obtain the AHI. Results of the polysomnographic sleep study are shown in Table 1. Flow Signal: Analysis was performed off-line. Data were exported in raw format and further processed using software specially developed for this study by one of the authors. The digitized flow signal was extracted from the raw data file of the sleep study. Flow-signal minima were identified automatically. The distance between two minima was taken as the duration of one breath. This marker was used instead of zero flow, because mimima were more reliable to detect, compared to the onset of flow. All breath-to-breath intervals were determined and displayed (Fig 1). The actual flow signals of all breaths were scanned on a monitor, and artifacts were corrected manually. Distribution of Breath-to-Breath Intervals: Means and standard deviations of all breath-to-breath intervals 10 s were calculated (ie, apneic episodes were not included). Furthermore, normal length of breath-to-breath intervals was defined by a duration within 50% of the median breath-to-breath interval duration. The distribution of these breath-to-breath intervals of normal length was analyzed for deviation from a normal distribution by specifying its kurtosis. The kurtosis of a distribution compares the shape of a distribution to a normal distribution (ie, the fact whether the distribution curve is more flat or more pointed, compared to a normal distribution). 19 If the distribution is normal, the value of kurtosis is, by definition, zero. A positive value of kurtosis indicates a sharp peak with longer tails including only few cases. That is, individual values are crowded together around the mean. A negative value indicates that the peak is flattened, compared to a normal distribution with many cases widely apart from the mean. Statistical analysis was performed using commercial software (Systat for Windows; SPSS; Chicago, IL). Data of measurements for single subjects were expressed as mean SD. Data of measurements between groups were expressed as mean SEM, Table 1 Polysomnographic Sleep Study* OSA Patients Variables Control Subjects (n 9) All (n 34) Without CPAP (n 27) Before CPAP (n 7) Age BMI AI AHI *Data are expressed as mean SD. Data show comparison of healthy subjects and patients with OSA. Patients with OSA (All) are classified in retrospect in a group that did not receive CPAP therapy (Without CPAP) and a group that eventually received CPAP therapy (Before CPAP) following the polysomnographic sleep study. BMI body mass index. p 0.05 control subjects vs OSA patients. 452 Clinical Investigations

3 Figure 1. Breath-to-breath intervals of the total recording period for OSA patient 2 (top, A) and OSA patient 12 (bottom, B), treated the first night with nasal CPAP. and the Mann-Whitney test was used for comparison. A p value of 0.05 was considered significant. Results Breathing Pattern in Healthy Subjects The breathing pattern in healthy subjects was very regular. Apneas did not occur, and the standard deviation for the duration of all nonapneic breaths was small. In addition, the distribution of all breathto-breath intervals within the normal range (ie, within 50% of the median breath-to-breath interval) was very narrow (Fig 2, left, A). This was expressed by a kurtosis significantly more than zero, indicating that the density distribution of the regular breath-to-breath intervals was more acute than expected for a normal distribution. Breathing Pattern in Untreated OSA Patients Breathing in OSA patients was characterized by frequent discontinuations. This became obvious in apneic breath-to-breath intervals of 10sinthe tachogram of all consecutive breath-to-breath intervals (Fig 1, top, A). However, nonapneic breathing in these patients was also altered by OSA. The standard deviations of these nonapneic breaths was increased, compared to that of healthy subjects. In addition, the frequency distribution of breath-to-breath intervals of normal length was also different from that of the control subjects. The kurtosis of this distribution was significantly lower compared to that in control subjects (Fig 3). This is equivalent to a less-regular breathing pattern, as was obvious in the analysis of breath-to-breath intervals of a single patient over the entire duration of sleep (Fig 2, middle, B) compared to a healthy subject (Fig 2, left, A). Compared to the total nighttime analysis, the same decrease in kurtosis was also found in periods that were not interrupted by apneas. One example would be the breath-tobreath intervals of 3,160 to 3,560 of patient 2 CHEST / 119 / 2/ FEBRUARY,

4 Figure 2. Distribution of all breath-to-breath intervals for control subject 1 (left, A) [kurtosis, 0.62], OSA patient 2 (middle, B) [kurtosis, 0.67], and OSA patient 12, (right, C) treated the first night with nasal CPAP (kurtosis, 1.20). (arrows in Fig 1, top, A) with a kurtosis of Age did not significantly influence kurtosis within the group of OSA patients (Table 2). Relation of Regularity of Breathing to Occurrence and Severity of OSA In patients with OSA, we found a significant positive correlation of AHI and the standard deviations of nonapneic breath-to-breath intervals (r 0.77; p ; Fig 4, top, A). In addition, the apnea index (AI; r 0.45; p ) as well as the AHI (r 0.64; p ) had significant negative correlations with the kurtosis of breath-to-breath frequency distribution (Fig 4, bottom, B). Although both AHI and AI correlated significantly with kurtosis of breath-to-breath frequency distribution, the correlation with AHI was considerably stronger. Influence of Sleep Stages on Breathing in Healthy Subjects and OSA Patients In healthy subjects, breathing became more regular with non-rem sleep and deep sleep stages, compared to wakefulness or REM sleep (Fig 5, top, A), indicated by an increase in kurtosis. For OSA patients, this increase in kurtosis with increased sleep stages was preserved, but compared to healthy subjects, kurtosis was significantly reduced in OSA patients in each sleep stage (Fig 5, top, A). Thus, changes in distribution of sleep stages with a small reduction of time spent in sleep stage 3 and sleep stage 4 (Fig 5, bottom, B) could not explain the reduction of kurtosis in OSA patients. In addition, we did not observe a difference in the time subjects were asleep between the groups (Fig 5, bottom, B). Breathing Pattern in OSA Patients With CPAP Therapy In OSA patients, where CPAP therapy was applied, episodes of apnea and hypopnea were promptly reduced during the first night of treatment. In the tachogram of all consecutive breath-to-breath intervals, the number of apneic intervals (ie, with a duration 10 s) dramatically decreased after the initial CPAP titration period (Fig 1, bottom, B), and thus the AI significantly decreased ( vs ; p 0.001). Together with the disappearance of frequent apneas, nonapneic breathing normalized. This more regular breathing is readily visible by comparing the 454 Clinical Investigations

5 (Fig 1, bottom, B). In line with this, the standard deviations of the length of these nonapneic breaths decreased ( ), compared to the untreated state ( ; p 0.02). The frequency distribution (Fig 2, right, C) of breath-to-breath intervals narrowed and was no longer different from that of control subjects. This was indicated by a significant (p 0.01) increase in kurtosis in treated vs untreated patients (Fig 3). In addition, kurtosis of treated patients no longer differed from that of control subjects (Fig 3). Discussion We found greater variation in nonapneic breathto-breath intervals in patients with OSA compared to healthy subjects. The extent of variation depended on the degree of disease. In addition to the presence of hypopnea and apnea, OSA is also characterized by a disturbance in the pattern of normal-length breaths. Figure 3. Comparison of kurtosis between OSA patients before and with nasal CPAP therapy and healthy subjects (mean SEM; *p 0.01, Mann-Whitney test). tachograms of breath-interval duration before (Fig 1, top, A) and after (Fig 1, bottom, B) CPAP therapy. CPAP treatment clearly reduced the width of the distribution around the mean duration of 3 s, resulting in a more narrow appearance of the black line Table 2 Polysomnographic Sleep Study and Kurtosis By Age* Variables Control yr (n 7) yr (n 17) OSA yr (n 17) Age BMI AI AHI Kurtosis *Data are expressed as mean SD. Patients with OSA are classified in two subgroups of younger and older patients and compared with older control subjects. p 0.05 control subjects vs OSA patients. p 0.05 younger vs older OSA patients. Breathing Pattern in Healthy Subjects Recurring changes in the rate of breathing was noticed 35 years ago in healthy subjects with short changes in respiratory frequency every three to four breaths superimposed on greater and more prolonged periodic changes. 20 These nonperiodic short changes could not be attributed solely to random fluctuations. The average length of increase and decrease in breathing frequency, and hence the factors controlling it, were to some extent characteristic for an individual subject. 21 This was later confirmed in identical twins, where pattern of breathing was significantly similar within twin pairs, 22 and in another study of healthy adults, where the unique characteristics of breathing pattern were maintained over a time period of 4 to 5 years. 23 Various mechanisms, which were previously reviewed, 24 are thought to cause temporal variations in the pattern of breathing. Periodic variations were contributed to oscillations originated in chemoreflex feedback loops and to central neural memory mechanisms. Nonrandom, nonperiodic variability of respiratory pattern was attributed to nonlinear interactions between pulmonary and airway afferent activities and integrative central respiratory mechanisms. Breath-by-breath variations decrease in non-rem sleep compared to wakefulness This is in accordance with our finding in healthy subjects that the distribution of breath-to-breath intervals was narrow during non-rem sleep. It has been reported that the inspiratory flow curve changes in patients with OSA from a regular CHEST / 119 / 2/ FEBRUARY,

6 Figure 4. Relation between AHI and standard deviation (top, A) and AHI and kurtosis (bottom, B) of breath-to-breath intervals in 9 control subjects (triangles) and 34 OSA patients (circles and linear regression line). sinusoidal shape to a more flattened shape during elevated upper-airway resistance. 28 This would influence measurements of mere inspiratory time and could lead to a wider distribution of breath intervals in OSA patients compared to healthy subjects. We therefore chose to analyze total interval duration only. Analyzing total interval duration is also supported by the fact that expiratory duration is linearly dependent on inspiratory duration. 29 However, there is no dependence of inspiratory duration on expiratory timing. 30 Breathing Pattern in OSA Patients The site of obstruction in OSA is thought to be the upper or lower pharynx, where the inward collapsing action of subatmospheric intrapharyngeal pressure during inspiration is normally prevented by an adequate tone of the genioglossus muscle. 31 In patients with OSA, maximum pharyngeal area tends to be decreased for a variety of reasons. 32 During wakefulness, this anatomic narrowing is compensated by increased genioglossal muscle tone. However, during sleep the compensation is lost. 33 Because these features are continuously present, they will also continuously affect breathing, although the extent may vary depending on the presence of confounding factors. After inspiratory resistive loading in healthy subjects, an increase in the scatter of inspiratory time intervals has been observed. 12 Magnitude and changes in scatter depend on the extent of load. Similarly, varying degrees of upper-airway narrowing in OSA patients during sleep 34 may be followed by an increase in breathing variability, as has been observed in the present study. Our finding of an increase in breathing variability in OSA patients cannot be explained by the appearance of a few breaths of extraordinary duration in comparison to a bulk of breath intervals that were unchanged, as may be suggested by a mere increase in standard deviations. Rather, this increase in 456 Clinical Investigations

7 Figure 5. Kurtosis (top, A) and duration (bottom, B) of different sleep stages in OSA patients (hatched circles/bars) and healthy subjects (open circles/bars) [mean SEM; *p 0.05]. breathing variability was caused by a change in the distribution of breath-to-breath intervals with normal length because kurtosis of these intervals was reduced. In addition, the increased fluctuation in the duration of each breath was not restricted to periods immediately after apnea, where a compensatory increase in breath flow is known to occur. Periods without apneic breathing also showed a reduction of kurtosis, compared to healthy subjects. The increased breathing variability of OSA patients was present during all sleep stages. Thus, the observed reduction in stage 3 and stage 4 sleep in OSA patients, where breathing variability is decreased in healthy subjects as well as in OSA patients, was not responsible for the increase in breathing variability in OSA patients. An age-dependent increase in breath-to-breath variability was suggested by some authors. 25,26,35 Therefore, an age-dependent difference in kurtosis between the two groups had to be excluded because the 10-year difference in mean age was statistically significant. Two factors oppose the possibility that the difference in breathing variability could be explained by age. First, within the group of patients with OSA, the younger and older patients did not differ in terms of mean kurtosis, as shown in Table 2. Secondly, the more severely affected OSA patients, who exhibited a greater breathing variability than the CHEST / 119 / 2/ FEBRUARY,

8 less affected OSA patients, tended to be even younger than the less affected patient group. The lack of an age-dependent effect in the present study might be explained by the relatively small difference in age between the two groups, compared to previous studies where an age dependence of breathing patterns was examined. In these studies, the age difference between the study groups was much greater (approximately 40 years), 25,26,35 the mean age of the younger subjects was much lower (25 to 29 years), and the mean age of the older subjects was much higher (69 to 76 years). Compared to the considerable variability in AHI for an individual patient, there appears to be a good correlation between kurtosis and AHI in the present study. This suggests that there might be a common underlying cause for the increase in breathto-breath variability and the increase in the severity of OSA. The link might be the increase in upperairway resistance, because a significant increase in breathing variability was also reported during snoring episodes leading to arousals in patients without OSA. 40 Increases in upper-airway resistance could thus lead to increases in breath-to-breath variability, but will finally result in apnea if a threshold value is reached. Effective CPAP therapy did not only reduce episodes of apnea and hypopnea, but also normalized breathing variability in the present study. This normalization of breathing pattern with the onset of CPAP therapy indicates that the altered breathing pattern in OSA patients is likely because of peripheral mechanisms such as the proposed increase in respiratory resistance, rather than to primarily central effects or to an altered chemoreceptor sensitivity. Analyzing the contour of the flow signal instead of its timing provided similar evidence for a relationship between flow limitations and changes in flow signal in a study 28 of CPAP titration in OSA patients. CPAP levels entirely eliminating upper-airway resistance in this study resulted in a regularly shaped inspiratory flow signal. In contrast, a high resistance was associated with a flattened inspiratory flow signal. The change to a more flattened flow contour remained detectable at suboptimal CPAP pressure that led to a decrease in apneic events with a concomitant increase in hypopneas. In this situation, esophageal pressure remained elevated. 41 A similar classification of the shape of inspiratory flow curves in 10 patients with various degrees of upper-airway resistance (ie, OSA, UARS, and snoring) also indicates a relation between upper-airway resistance and changes in breathing pattern, 11 as is suggested by our observations. Additional strength is added to this interpretation by a stronger correlation of kurtosis with AHI rather than AI alone in the present study. The greater variation in breath-to-breath interval described in this study may constitute a characteristic feature of OSA. Although a direct relation between UARS and increased breath-to-breath variability remains to be shown, we have demonstrated significantly increased breath-to-breath variability in OSA patients correlating to the extent of upperairway obstruction as indicated by AHI. This variability resolved immediately with the onset of effective CPAP therapy. Analysis of breath-to-breath intervals might therefore prove to be a valuable diagnostic tool to assess upper-airway obstruction during sleep. It seems likely that increased resistance without overt obstruction, as is characteristic for UARS, can be detected with this technique. However, this will have to await further evaluation. In the future it may be possible to incorporate breathing variability into automated control algorithm for CPAP adjustment in order to avoid not only overt complete airway obstruction by autoadjusted CPAP, but also the increase in upper-airway resistance that might be indicated by increased breathing variability. References 1 Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328: Strollo PJ Jr, Rogers RM. Obstructive sleep apnea. N Engl J Med 1996; 334: Shepard JWJ. Cardiopulmonary consequences of obstructive sleep apnea. Mayo Clin Proc 1990; 65: Nasser S, Rees PJ. Sleep apnoea: causes, consequences and treatment. Br J Clin Pract 1992; 46: He J, Kryger MH, Zorick FJ, et al. Mortality and apnea index in obstructive sleep apnea: experience in 385 male patients. Chest 1988; 94: Badr MS. Pathogenesis of obstructive sleep apnea. Prog Cardiovasc Dis 1999; 41: Loube DI, Andrada TF. 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9 15 Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Los Angeles, CA: Brain Information Service/ Brain Research Institute, EEG arousals: scoring rules and examples; a preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep 1992; 15: Moser NJ, Phillips BA, Berry DT, et al. What is hypopnea, anyway? Chest 1994; 105: Gottlieb DJ, Whitney CW, Bonekat WH, et al. Relation of sleepiness to respiratory disturbance index: the Sleep Heart Health Study. Am J Respir Crit Care Med 1999; 159: Press WH, Flannery BP, Teukolsky SA, et al. Statistical description of data. In: Numerical recipes in Pascal. 1st ed. Cambridge, UK: Cambridge University Press 1989; Priban IP. An analysis of some short-term patterns of breathing in man at rest. J Physiol 1962; 166: Shea SA, Walter J, Murphy K, et al. Evidence for individuality of breathing patterns in resting healthy man. Respir Physiol 1987; 68: Shea SA, Benchetrit G, Dinh TP, et al. The breathing patterns of identical twins. Respir Physiol 1989; 75: Benchetrit G, Shea SA, Dinh TP, et al. Individuality of breathing patterns in adults assessed over time. Respir Physiol 1989; 75: Bruce EN. Temporal variations in the pattern of breathing. J Appl Physiol 1996; 80: Shore ET, Millman RP, Silage DA, et al. Ventilatory and arousal patterns during sleep in normal young and elderly subjects. J Appl Physiol 1985; 59: Tobin MJ, Mador MJ, Guenther SM, et al. Variability of resting respiratory drive and timing in healthy subjects. J Appl Physiol 1988; 65: Morrell MJ, Harty HR, Adams L, et al. Breathing during wakefulness and NREM sleep in humans without an upper airway. J Appl Physiol 1996; 81: Condos R, Norman RG, Krishnasamy I, et al. Flow limitation as a noninvasive assessment of residual upper-airway resistance during continuous positive airway pressure therapy of obstructive sleep apnea. Am J Respir Crit Care Med 1994; 150: Clark FJ, von Euler C. On the regulation of depth and rate of breathing. J Physiol 1972; 222: Rafferty GF, Evans J, Gardner WN. Control of expiratory time in conscious humans. J Appl Physiol 1995; 78: Remmers JE, DeGroot WJ, Sauerland EK, et al. Pathogenesis of upper airway occlusion during sleep. J Appl Physiol 1978; 44: Isono S, Remmers JE, Tanaka A, et al. Anatomy of pharynx in patients with obstructive sleep apnea and in normal subjects. J Appl Physiol 1997; 82: Remmers JE, Lahiri S. Regulating the ventilatory pump: a splendid control system prone to fail during sleep. Am J Respir Crit Care Med 1998; 157:S95 S Schwartz AR, O Donnell CP, Baron J, et al. The hypotonic upper airway in obstructive sleep apnea. Am J Respir Crit Care Med 1998; 157: Tobin MJ, Chadha TS, Jenouri G, et al. Breathing patterns: 1. Normal subjects. Chest 1983; 84: Parra O, García-Esclasans N, Montserrat JM, et al. Should patients with sleep apnoea/hypopnoea syndrome be diagnosed and managed on the basis of home sleep studies? Eur Respir J 1997; 10: Fanfulla F, Patruno V, Bruschi C, et al. Obstructive sleep apnoea syndrome: is the half-night polysomnography an adequate method for evaluating sleep profile and respiratory events? Eur Respir J 1997; 10: Wittig RM, Romaker A, Zorick FJ, et al. Night-to-night consistency of apneas during sleep. Am Rev Respir Dis 1984; 129: Mosko SS, Dickel MJ, Ashurst J. Night-to-night variability in sleep apnea and sleep-related periodic leg movements in the elderly. Sleep 1988; 11: Bloch KE, Li Y, Sackner MA, et al. Breathing pattern during sleep disruptive snoring. Eur Respir J 1997; 10: Montserrat JM, Ballester E, Olivi H, et al. Time-course of stepwise CPAP titration: behavior of respiratory and neurologic variables. Am J Respir Crit Care Med 1995; 152: CHEST / 119 / 2/ FEBRUARY,

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