Analysis of the sleep EEG in the complexity domain

Conventional sleep analysis relies primarily on electroencephalogram (EEG) waveform features assessed in concert with eye movements, respiration and muscle tone. We explore a complementary “complexity domain” approach based on multiscale entropy (MSE) analysis of EEG signals and discuss its relationships to standard sleep analysis and to that based on electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC). We observe a progressive decrease in complexity associated with decreased arousability, as measured by both conventional sleep scoring and CPC analysis. Furthermore, complexity analysis supports the contention that stage 2 non-REM sleep has distinct sub-phases that map to CPC high- and low-frequency coupled dynamics.

[1]  Sheng-Fu Liang,et al.  Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models , 2012, IEEE Transactions on Instrumentation and Measurement.

[2]  R. Thomas,et al.  Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep. , 2014, Sleep medicine.

[3]  Ary L. Goldberger,et al.  Complex Systems , 2006, Intelligenza Artificiale.

[4]  J. Hanley,et al.  Statistical analysis of correlated data using generalized estimating equations: an orientation. , 2003, American journal of epidemiology.

[5]  R. Thomas,et al.  Cyclic alternating pattern in the electroencephalogram: what is its clinical utility? , 2007, Sleep.

[6]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  N. Nicolaou,et al.  The Use of Permutation Entropy to Characterize Sleep Electroencephalograms , 2011, Clinical EEG and neuroscience.

[8]  Joseph E. Mietus,et al.  Mapping sleep using coupled biological oscillations , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Chaur-Jong Hu,et al.  Multiscale Entropy Analysis of Electroencephalography During Sleep in Patients With Parkinson Disease , 2013, Clinical EEG and neuroscience.

[10]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[11]  M. Terzano,et al.  Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. , 2002, Sleep medicine.

[12]  Sara Mariani,et al.  Use of multiscale entropy to facilitate artifact detection in electroencephalographic signals , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  E. Bruce,et al.  Sample Entropy Tracks Changes in Electroencephalogram Power Spectrum With Sleep State and Aging , 2009, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[14]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[15]  A. Rechtschaffen,et al.  A manual of standardized terminology, technique and scoring system for sleep stages of human subjects , 1968 .

[16]  S. Leistedt,et al.  Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis , 2007, Clinical Neurophysiology.

[17]  M Nakamura,et al.  Elimination of EKG artifacts from EEG records: a new method of non-cephalic referential EEG recording. , 1987, Electroencephalography and clinical neurophysiology.

[18]  R. Thomas,et al.  An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. , 2005, Sleep.

[19]  E. Wolpert A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[20]  José Luis Rodríguez-Sotelo,et al.  Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques , 2014, Entropy.

[21]  K. A. Loparo,et al.  Nonlinear dynamical analysis of the neonatal EEG time series: The relationship between sleep state and complexity , 2008, Clinical Neurophysiology.