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.

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague 160 00 Prague 6 Czech Republic

Department of Computing and Control Engineering University of Chemistry and Technology Prague 166 28 Prague 6 Czech Republic

Department of Neurology Faculty of Medicine in Hradec Králové Charles University 500 05 Hradec Králové Czech Republic

Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex Colchester CO4 3SQ UK

School of Science and Technology Nottingham Trent University Nottingham NG11 8NS UK

Smart Health Technologies Group School of Computer Science and Electronic Engineering; University of Essex Colchester CO4 3SQ UK

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