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.

• MeSH
• Algorithms MeSH
• Biosensing Techniques * MeSH
• Deep Learning * MeSH
• Respiration MeSH
• Electrocardiography MeSH
• Entropy MeSH
• Humans MeSH
• Sleep Apnea, Obstructive MeSH
• Signal Processing, Computer-Assisted * MeSH
• Polysomnography MeSH
• Sleep Wake Disorders * MeSH
• Heart Rate MeSH
• Wavelet Analysis MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH