Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning
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 movem...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-05-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/9/2594 |
id |
doaj-c1712abfa2a24bc9bf9f139e757e216c |
---|---|
record_format |
Article |
spelling |
doaj-c1712abfa2a24bc9bf9f139e757e216c2020-11-25T02:13:04ZengMDPI AGSensors1424-82202020-05-01202594259410.3390/s20092594Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep LearningDelaram Jarchi0Javier Andreu-Perez1Mehrin Kiani2Oldrich Vysata3Jiri Kuchynka 4Ales Prochazka5Saeid Sanei6Smart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UKSmart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UKSmart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UKDepartment of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech RepublicDepartment of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech RepublicDepartment of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech RepublicSchool of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UKAccurately 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.https://www.mdpi.com/1424-8220/20/9/2594electrocardiographyelectromyographypolysomnographyrespiratory modulationsynchrosqueezed wavelet transform |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Delaram Jarchi Javier Andreu-Perez Mehrin Kiani Oldrich Vysata Jiri Kuchynka Ales Prochazka Saeid Sanei |
spellingShingle |
Delaram Jarchi Javier Andreu-Perez Mehrin Kiani Oldrich Vysata Jiri Kuchynka Ales Prochazka Saeid Sanei Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning Sensors electrocardiography electromyography polysomnography respiratory modulation synchrosqueezed wavelet transform |
author_facet |
Delaram Jarchi Javier Andreu-Perez Mehrin Kiani Oldrich Vysata Jiri Kuchynka Ales Prochazka Saeid Sanei |
author_sort |
Delaram Jarchi |
title |
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning |
title_short |
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning |
title_full |
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning |
title_fullStr |
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning |
title_full_unstemmed |
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning |
title_sort |
recognition of patient groups with sleep related disorders using bio-signal processing and deep learning |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-05-01 |
description |
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. |
topic |
electrocardiography electromyography polysomnography respiratory modulation synchrosqueezed wavelet transform |
url |
https://www.mdpi.com/1424-8220/20/9/2594 |
work_keys_str_mv |
AT delaramjarchi recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning AT javierandreuperez recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning AT mehrinkiani recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning AT oldrichvysata recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning AT jirikuchynka recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning AT alesprochazka recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning AT saeidsanei recognitionofpatientgroupswithsleeprelateddisordersusingbiosignalprocessinganddeeplearning |
_version_ |
1724906449661853696 |