Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea

• Published in: IEEE transactions on biomedical engineering Vol. 53; no. 3; pp. 485 - 496
• Main Authors: Redmond, S.J., Heneghan, C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biomedical measurements; Breathing; Cardiology; Diagnosis, Computer-Assisted - methods; ECG; EEG; Electrocardiography; Electrocardiography - methods; Electroencephalography; Female; Heart Rate; Humans; Inductance; Male; Middle Aged; obstructive sleep apnea; Pattern Recognition, Automated - methods; Plethysmography; Polysomnography - methods; Reproducibility of Results; respiration; Respiratory Mechanics; Robustness; Sensitivity and Specificity; Sleep apnea; Sleep Apnea, Obstructive - diagnosis; Sleep disorders; sleep stage; Sleep Stages; Studies; Testing; Training data
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2005.869773

A cardiorespiratory-based automatic sleep staging system for subjects with sleep-disordered breathing is described. A simplified three-state system is used: Wakefulness (W), rapid eye movement (REM) sleep (R), and non-REM sleep (S). The system scores the sleep stages in standard 30-s epochs. A number of features associated with the epoch RR-intervals, an inductance plethysmography estimate of rib cage respiratory effort, and an electrocardiogram-derived respiration (EDR) signal were investigated. A subject-specific quadratic discriminant classifier was trained, randomly choosing 20% of the subject's epochs (in appropriate proportions of W, S and R) as the training data. The remaining 80% of epochs were presented to the classifier for testing. An estimated classification accuracy of 79% (Cohen's /spl kappa/ value of 0.56) was achieved. When a similar subject-independent classifier was trained, using epochs from all other subjects as the training data, a drop in classification accuracy to 67% (/spl kappa/=0.32) was observed. The subjects were further broken in groups of low apnoea-hypopnea index (AHI) and high AHI and the experiments repeated. The subject-specific classifier performed better on subjects with low AHI than high AHI; the performance of the subject-independent classifier is not correlated with AHI. For comparison an electroencephalograms (EEGs)-based classifier was trained utilizing several standard EEG features. The subject-specific classifier yielded an accuracy of 87% (/spl kappa/=0.75), and an accuracy of 84% (/spl kappa/=0.68) was obtained for the subject-independent classifier, indicating that EEG features are quite robust across subjects. We conclude that the cardiorespiratory signals provide moderate sleep-staging accuracy, however, features exhibit significant subject dependence which presents potential limits to the use of these signals in a general subject-independent sleep staging system.