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

Jarchi, Delaram and Andreu-Perez, Javier and Kiani, Mehrin and Vysata, Oldrich and Kuchynka, Jiri and Prochazka, Ales and Sanei, Saeid (2020) 'Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.' Sensors, 20 (9). ISSN 1424-8220

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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.

Item Type: Article
Uncontrolled Keywords: electrocardiography; electromyography; polysomnography; respiratory modulation; synchrosqueezed wavelet transform
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 19 Aug 2020 15:44
Last Modified: 19 Aug 2020 16:15
URI: http://repository.essex.ac.uk/id/eprint/27443

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