Title: Automatic classification of slow-wave sleep and REM-sleep stages using somnographic ECG signal: some preliminary results for obese and no-obese patients

Authors: S. Khemiri; K. Aloui; M.S. Naceur

Addresses: LTSIRS Remote Sensing and GIS Laboratory, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, BP 37, Le Belvédère 1002 Tunis, Tunisia ' LTSIRS Remote Sensing and GIS Laboratory, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, BP 37, Le Belvédère 1002 Tunis, Tunisia ' LTSIRS Remote Sensing and GIS Laboratory, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, BP 37, Le Belvédère 1002 Tunis, Tunisia

Abstract: A new approach of automatic classification of slow-wave sleep and REM sleep is proposed in this paper. Our approach is based on automatic classification of polysomnographic ECG signals on sleep stages. This method is applied to obese persons (weight of more than 100 kg) and no-obese persons (weight of less than 100 kg). We show the pre-processing technique of the ECG signals and, at the same time, the identification and elimination of the different types of artefacts which are contained in the signal. The automatic classification of the slow-deep sleep and the rapid eye movement sleep called in this work REM sleep consists of extracting physiological indicators that characterise these two sleep stages through the polysomnographic ECG signal. In other words, this classification is based on the analysis of the cardiac rhythm during a night's sleep. We tested our approach on ECG signals from the PHYSIOBANK database. Our experimental results show the significance of this classification method. In fact, we arrive at a precision in the order of 92.28% in the classification of slow-wave sleep and in the order of 91.82% in the classification of REM sleep. We calculated the precision of the classification compared to the results of the experts who used four physiological signals: EEG, EOG, EMG and ECG.

Keywords: artefacts; REM sleep; rapid eye movement; polysomnographic ECG signals; automatic classification; slow wave sleep; somnographic ECG signals; obese patients; non-obese patients; obesity; electroencephalography; cerebral activity; electrooculography; EOG; eye movements; electromyography; EMG; muscular activity; electrocardiograms; ECG; cardiac rhythms; heart rate variation.

DOI: 10.1504/IJSISE.2015.067063

International Journal of Signal and Imaging Systems Engineering, 2015 Vol.8 No.1/2, pp.11 - 19

Received: 17 Oct 2012
Accepted: 04 Oct 2013
Published online: 25 Jan 2015 *

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