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Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

D. Jarchi, J. Andreu-Perez, M. Kiani, O. Vysata, J. Kuchynka, A. Prochazka, S. Sanei

. 2020 ; 20 (9) : . [pub] 20200502

Language English Country Switzerland

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc21012618

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

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$a Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.
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$a Andreu-Perez, Javier $u Smart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UK $u Embedded and Intelligent Systems Laboratory, School of Computer Science and Electronics, University of Essex, Colchester CO4 3SQ, UK
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$a Kiani, Mehrin $u Smart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UK
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$a Vysata, Oldrich $u Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic $u Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic
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$a Kuchynka, Jiri $u Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic
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$a Prochazka, Ales $u Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic $u Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic
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$a Sanei, Saeid $u School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
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