Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.
electrocardiography
electromyography
polysomnography
respiratory modulation
synchrosqueezed wavelet transform
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
02 May 2020
02 May 2020
Historique:
received:
20
03
2020
revised:
20
04
2020
accepted:
28
04
2020
entrez:
7
5
2020
pubmed:
7
5
2020
medline:
12
2
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32370185
pii: s20092594
doi: 10.3390/s20092594
pmc: PMC7248846
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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