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Sanei, S. 
ORCID: 0000-0002-3437-2801, Jarchi, D., Procházka, A. and Vyšata, O.,
2020.
Recognition of patient groups with sleep related disorder using bio-signal processing and deep learning.
Sensors, 20: 2594.
ISSN 1424-822
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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: | Journal article | ||||||
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| Publication Title: | Sensors | ||||||
| Creators: | Sanei, S., Jarchi, D., Procházka, A. and Vyšata, O. | ||||||
| Publisher: | MDPI-AG | ||||||
| Date: | 2 May 2020 | ||||||
| Volume: | 20 | ||||||
| ISSN: | 1424-822 | ||||||
| Identifiers: |
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| Rights: | c 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Linda Sullivan | ||||||
| Date Added: | 04 May 2020 11:15 | ||||||
| Last Modified: | 04 May 2020 11:15 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/39782 |
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