Edson F. Estrada Ph.D. Student Homer Nazeran Ph.D.

Transcription

1 Edson F. Estrada Ph.D. Student Homer Nazeran Ph.D. Department of Electrical & Computer Engineering The University of Texas at El Paso

2 SLEEP Circadian rhythm is essential for human life [1] Blood pressure fluctuates Heart rate slows down Muscles relax Metabolic rate decreases Hormone fluctuation The main difference between rest and sleep lies in the brain, which controls the sleep stage by the reduction of the body movements and a decrease of the alertness of the environment Physiologic significance: To repair muscles and other tissues To replace aging or dead cells, etc

3 Polysomnogram and Biosignals [2] Electroencephalogram EEG Electrooculogram EOG Electromiogram EMG Electrocardiogram ECG or EKG HRV Airflow, Respiratory Effort, Oxygen Saturation, Temperature, and body position

4 Heart Rate Variability (HRV) [3]

5 Sleep Stages and R & K Rules [4] A standard system for visually scoring stages of sleep N-REM Stage 1-4 REM (Rapid Eye Movements)

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8 Sleep Apnea [5] It is defined as cessation of breathing for 20 seconds or longer (or for a shorter period of time if accompanied by bradycardia, cyanosis, or pallor) Central Obstructive OSA Mixed Sleep Apnea

9 Significant efforts have been executed to provide physicians with reliable-less-invasive tools to automatically classify the sleep stages Technicians are not confident enough to trust because of the lack of accuracy on those equipments visual scoring is still preferred over automatic methods Similar attempts have been performed to diagnose Sleep Apnea Processes have been treated as isolated problems to solve. Sleep Staging Sleep Apnea Sleep Disorder

10 Sleep-related problems affect more than 50 to 70 million Americans [14]. 18 million American adults have sleep apnea Lack of sleep reduces our alertness, impairs our judgment, and affects our tempers. Life Quality Lost of productivity Accidents The National Highway Traffic Safety Administration (NHTSA) estimated that 100,000 to 150,000 automotive accidents each year of which four percent is caused by drowsy driving National Sleep Foundation (NSF) in 2008 Sleep in America Poll [15], responders answered that 29 percent fell asleep or become very sleepy at work during the last month of the survey, and 36 percent have fallen asleep or nodded off while driving in the past year.

11 A large part of the subjects suffering from sleep apnea does not have a diagnostic and therefore lack of the appropriate treatment. the IMSS has practiced a 1,489 polysomnogram studies, an average of 362 examinations per year. Another study carried out by the IMSS found that 35 million of Mexicans from ages between ages 30 and 60 suffered from snoring and it is consider as an important public health problem [18]. The Mexican Institute of Social Security (IMSS Instituto Mexicano del Seguro Social) states that sleep apnea is one of more 80 sleep disorders that affect 4 million of Mexicans [17] INER establishes a significant sleep apnea prevalence on a population older than 40 years; 2.2 percent for women and 4.4 percent for men [16]

12 Sleep Apnea Obesity, arrhythmias, high blood presure, metabolic sindrome, diabetes Co-morbidity

13 The National Center of Research Sleep (NCRS) recommends essential directives on Sleep-wake stage research topics [19] To allow the medical community by means of new and sophisticated computer-based signal processing methodologies (both linear and non linear approaches), to timely diagnose and treat patients on an early stage of the disorder Must be enable the design of portable ambulatory systems to measure sleep and other physiologic variables at home providing high accuracy measurements

14 Independent The understanding of the relationships between EEG and HRV features during overnight polysomraphy may improve the performance of the sleep classification and apnea detection over current algorithms. Biosignal tracings manual scoring apnea events annotations Feature Extraction Algorithms Classification system Features Statistical Correlation Statistical diferences Performance Accuracy Sensibility Specificity Dependent

15 To identify relationships among sleep stages and apneic events by means of EEG and HRV features in order to increase sleep classification and apnea detection algorithms performance

16 Data collection Sleep Centers At least 18 years of age Possible diagnosis of obstructive sleep apnea, central sleep apnea or primary snoring diagnosed No cardiac disease reported Autonomic Dysfunction Not on medication known to interfere with heart rate Polysomnogram record missing any biosignal used in this study Sleep Database Biosignals Manual Scorings Apnea Events Annotations General Information Physionet.org 25 Patients INER 25 Patients Local Sleep Labs

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18 A) Relative Percent Spectral Energy Band (RPEB) and Harmonic Hjorth Parameters [20] RPEB Delta 1 (p<0.01) RPEB Alpha (p<0.01) Central Frequency (p>0.01)

19 Hjorth Parameters Activity Mobility Complexity Activity (p<0.01) Mobility (p<0.01) Complexity (p<0.01)

20 Itakura distance [21] EEG Vector Epoch y[n] Distance 2 Epoch y[n] Baseline x[n] Distance 1 Itakura Distance (p<0.01)

21 Detrended Fluctuation Analysis (DFA) [22] DFA-alpha (p<0.01)

22 Correlation Dimension (D2) [23] The term dimension measures the geometry of a cloud of constructed points from a time series in the phase-space Correlation Dimension (p<0.01)

23 Wavelets A wavelet packet tree (WPT) of depth 8 was designed to extract the first feature set. Daubechies order 2 wavelet transform was applied to the 30-sec epochs of EEG signal. Out of the family of generated sub-bands, those containing frequency information of delta, theta, alpha, spindle, beta1, and beta2 bands were selected. Self Organization Maps - neural network to Time-Frequency features

24 R-R statistical Analysis [9]. Max RR, Min RR, Mean RR, SDNN (standard deviation of R to R intervals) and Variance.

25 Frequency-domain parameters Classical band energies will be extracted from HRV: VLF, LF, HF, LFnorm, HFnorm, LF/HF ratio. Aproximate Entropy (ApEn) [24] DFA D2

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27 Fundaments for more consolidated sleep stage classification and apneic event detection algorithm will be enhanced. More accurate techniques may allow timely diagnosing and treating sleep apnea. Less invasive methods (minimal number of biosignal required) enables the future of reliable portable devices. Stronger sleep stage classification algorithms may not only be used on subjects suffering from sleep apnea but on other sleep disorders.

28 EMG feature extraction EOG feature extraction

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30 [1] Wallace B. Mendelson, Human Sleep, Research and Clinical Care, Plenum Medical Book Company, New York and London *2+ John G. Webster, Medical Instrumentation, Application and Design, Wiley, Third Edition [3] Task Force of The European Society of Cardiology and The North American Society of Pacing and Electro physiology, Heart rate variability. Standards of Measurement, physiological interpretation, and clinical use, Eur. Heart J., vol.17, pp , 1996 [4]Rechtschaffen A. Kales A. Rechtschaffen A. Kales A, eds. A Manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Los Angeles: Brain Information service/brain Research Institute, 1968 *5+ American Academy of Sleep Medicine. The International Classification of Sleep Disorders, Revised Diagnostic and Coding Manual, Westchester, [6] R. Agarwal, J. Gotman. Computer-Assisted Sleep Staging IEEE TRANSACTIONS On Biomedical Engineering, 2001,VOL. 48, NO. 12. [7] E. Oropesa, HL. Cycon, M. Jobert "Sleep Stage Classification using Wavelet Transform and Neural Network",ICSI Technical Report 1999, tr [8] JE. Heiss, C.M. Held, P.A. Estévez, C.A. Perez, CA. Holzmann, JP. Pérez. Classification of Sleep Stages in Infants:A Neuro Fuzzy Approach. IEEE Engineering In Medicine And Biology, 21-5, [9] ED. Übeyli, Statistics over features of ECG signals Expert Systems with Applications, 2009, 36, [10] E. Komatsu, Y. Kurihara, K. Watanabe. Sleep Stage Estimation by Non-invasive Bio-measurement. SICE-ICASE International Joint Conference [11] Penzel, K. Kesper, V. Gross, H.F. Becker, C. Vogelmeier. Problems in Automatic Sleep Scoring Applied to Sleep Apnea. Proceedings of the 25th Annual International Conference of the IEEE EMBS 2003, [12] A. Khandoker, C. Karmakar, M. Palaniswami, Analysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events Computers in Cardiology 2008;35: [13] Y. Ichimaru, K. Clark, J. Ringler, W. Weiss. EFFECT OF SLEEP STAGE ON THE RELATIONSHIP BETWEEN RESPIRATION AND HEART RATE VARIABILITY Computers in Cardiology 1990, Proceedings.Volume, Issue, Sep 1990 Page(s):

31 [14] U.S. Department of Health and Human Services, 2003 National Sleep Institutes of Health Publication No Washington, DC: HHS, [15] 2008 Sleep in America Poll Results, National Sleep Foundation. [Online] [Accessed: April, 2009] [16] Resumén Apnea del Sueño, Instituto Nacional de Enfermedades Respiratorias [Onine] [Accessed: April, 2009]. [17] Comunicado IMMS. La Apnea Del Sueño Afecta A Más De Cuatro Millones De Personas En México, 2007, No [18] Comunicado IMMS. Roncar No Significa Dormir Bien, 35 Millones De Mexicanos Sufren Esta Enfermedad, 2007, No [19]2003 Nationalsleep Disorders Research Plan National Center on Sleep Disorders Research [Online] [Accessed: April, 2009] [20] P. Van Hese, W. Philips, J. De Koninck. R.Van de Walle, and I. Lemahieu. Automatic Detection of Sleep Stages Using the EEG. Proceedings of the 23rd Annual EMBS International Conference, October 25-28, Istanbul, Turkey, 2001, pp [21] A. Rezek, Stephen J. Roberts, Parametric Model Order Estimation: A Brief Review Model Based Digital Signal Processing Techniques in the Analysis of Biomedical Signals (Digest No. 1997/009), IEE Colloquium on the Use of 16 April 1997 pp 3/1-3/6. [22] Rudolph C. Hwa and Thomas C. Ferree. Fluctuation Analysis of Human Electroencephalogram. Nonlinear Phenomena in Complex Systems, 5:3 (2002) [23] N. Burioka, G. Cornélissen, F. Halberg, D. Kaplan, Relationship between Correlation Dimension and indices of linear analysis in both respiratory movement and electroencephalogram. Clinical Neurophysiology Journal, vol. 112, pp , *24+ G B Moody, Approximate Entropy (ApEn), Physio Toolkit,