Review Received: October 27, 2015 Accepted: October 27, 2015 Published online: December 10, 2015
Eur Neurol 2015;74:268–287 DOI: 10.1159/000441975
Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection U. Rajendra Acharya a, c Shreya Bhat d Oliver Faust e Hojjat Adeli f–k Eric Chern-Pin Chua b Wei Jie Eugene Lim a Joel En Wei Koh a
a
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic and b Singapore Institute of Technology, Singapore, Singapore; c Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; d Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, India; e School of Science and Engineering, Habib University, Karachi, Pakistan; Departments of f Neuroscience, g Neurology, h Biomedical Engineering, i Biomedical Informatics, j Civil, Environmental, and Geodetic Engineering, and k Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA
Abstract Background: The brain’s continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEGbased sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five differ-
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ent sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical Fvalue, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment. © 2015 S. Karger AG, Basel
1. Introduction
Sleep is a naturally occurring state of meditation characterized by distorted consciousness and immobility of voluntary and sensory muscles, but the human brain is continuously processing during sleep. Sleep U. Rajendra Acharya Department of ECE, Ngee Ann Polytechnic 535 Clementi Road Singapore 599489 (Singapore) E-Mail aru @ np.edu.sg
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Key Words Sleep apnea · EEG · REM sleep · NREM sleep · Nonlinear · Variability · Recurrence quantification analysis
Stages
Eye movement
EEG variation
Stage 0 (wake)
Eyes are open
EEG varies rapidly Prominent beta activity with 13–26 Hz frequency and low voltage of 10–30 μV Alpha activity with 8–12 Hz frequency and higher voltage of 20–40 μV
Stage 1 (drowsiness) Slow movements of eye rolling
Alpha waves (8–12 Hz) disappear Theta waves (4–7 Hz) appear
Stage 2 (light sleep)
Eye movement stops
Burst of brain activity visible on EEG Sleep spindles (11–15 Hz) and K-complexes appear on the background of theta waves
Stage 3 (deep sleep)
–
Delta waves appear slowly with EEG amplitude >75 μV and 1–3 Hz frequency Sleep spindles and K-complexes also exist
Stage 4 (deep/slow wave sleep)
–
Prominent delta waves with frequency
