Sleep stage classification with ECG and respiratory effort.
Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that are currently available, promising the application for personal and continuous home sleep monitoring. This paper describes a methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis. A total of 142 features were extracted from electrocardiogram and thoracic respiratory effort measured with respiratory inductance plethysmography. To improve the quality of these features, subject-specific Z-score normalization and spline smoothing were used to reduce between-subject and within-subject variability. A modified sequential forward selection feature selector procedure was applied, yielding 80 features while preventing the introduction of bias in the estimation of cross-validation performance. PSG data from 48 healthy adults were used to validate our methods. Using a linear discriminant classifier and a ten-fold cross-validation, we achieved a Cohen's kappa coefficient of 0.49 and an accuracy of 69% in the classification of wake, REM, light, and deep sleep. These values increased to kappa = 0.56 and accuracy = 80% when the classification problem was reduced to three classes, wake, REM sleep, and NREM sleep.
Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands. Department of Electrical Engineering, Eindhoven University of Technology, Postbus 513, 5600MB Eindhoven, The Netherlands.
Pedro
P
Fonseca
Xi
X
Long
Mustafa
M
Radha
Reinder
R
Haakma
Ronald M
RM
Aarts
Jérôme
J
Rolink
D000328:Adult; D001341:Autonomic Nervous System / Q000502:physiology; D004562:Electrocardiography; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages