Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning
This work addresses the need for accurate diagnosis of sleep disorders like OSA and RLS for patients, but it is incremental as it builds on existing bio-signal processing and deep learning methods.
The researchers tackled the problem of diagnosing sleep disorders by developing a deep learning framework that uses ECG and EMG bio-signals to classify patients into four groups, achieving a mean accuracy of 72% and a weighted F1 score of 0.57.
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.