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Accurately diagnosing sleep disorders is essential for clinical assessmentsand treatments. Polysomnography (PSG) has long been used for detection ofvarious sleep disorders. In this research, electrocardiography (ECG) andelectromayography (EMG) have been used for recognition of breathing andmovement-related sleep disorders. Bio-signal processing has been performed byextracting EMG features exploiting entropy and statistical moments, in additionto developing an iterative pulse peak detection algorithm using synchrosqueezedwavelet transform (SSWT) for reliable extraction of heart rate andbreathing-related features from ECG. A deep learning framework has beendesigned to incorporate EMG and ECG features. The framework has been used toclassify four groups: healthy subjects, patients with obstructive sleep apnea(OSA), patients with restless leg syndrome (RLS) and patients with both OSA andRLS. The proposed deep learning framework produced a mean accuracy of 72% andweighted F1 score of 0.57 across subjects for our formulated four-classproblem.

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