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

TL;DR

This paper presents a deep learning approach utilizing ECG and EMG bio-signals to classify sleep disorder patient groups with an accuracy of 72%, aiding clinical diagnosis.

Contribution

It introduces a novel bio-signal processing method combined with a deep learning framework for sleep disorder classification using ECG and EMG features.

Findings

Achieved 72% mean accuracy in classifying four sleep disorder groups.

Developed an iterative pulse peak detection algorithm using SSWT.

Demonstrated effective recognition of sleep-related disorders from bio-signals.

Abstract

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.