TELKOMNIKA Telecommunication, Computing, Electronics and Control
An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques
An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques
Subject
Multivariate regression
Obstructive sleep apnea
Photoplethysmogram
Random forest
Support vector machine
Univariate regression
Obstructive sleep apnea
Photoplethysmogram
Random forest
Support vector machine
Univariate regression
Description
Obstructive sleep apnea (OSA), a very common sleep disorder remains as an
underdiagnosed root cause for several cardiovascular and cerebrovascular
diseases. In this paper, we propose an efficient and accurate system that
utilizes a single sensor for effective screening of OSA using machine
learning algorithms. The automatic screening system involves a
photoplethysmogram (PPG) signal, a novel algorithm to detect and remove
the corrupted part of the signal, a feature extraction module to extract several
features from the PPG waveform and a classifier module which helps in
screening for OSA. The elemental idea behind this work is that there is a
characteristic relationship between the shape of the PPG waveform and the
oxygen desaturation in the apnea patients. The method as described was
tested on 285 subjects, inclusive of both normal and apnea patients, and the
results were obtained after 10-fold-cross validation of the different machine
learning techniques viz., univariate regression, multivariate regression,
support vector machine and random forest. The best results in screening OSA
were obtained from random forest algorithm with the highest performance
(Acc: 98.0%, Sen: 98.6%, Spec: 99.3%) for all the combined features. The
proposed work is an effective system for automatic screening of OSA from a
single PPG sensor, thereby reducing the need for a very expensive and
overnight polysomnography sleep study.
underdiagnosed root cause for several cardiovascular and cerebrovascular
diseases. In this paper, we propose an efficient and accurate system that
utilizes a single sensor for effective screening of OSA using machine
learning algorithms. The automatic screening system involves a
photoplethysmogram (PPG) signal, a novel algorithm to detect and remove
the corrupted part of the signal, a feature extraction module to extract several
features from the PPG waveform and a classifier module which helps in
screening for OSA. The elemental idea behind this work is that there is a
characteristic relationship between the shape of the PPG waveform and the
oxygen desaturation in the apnea patients. The method as described was
tested on 285 subjects, inclusive of both normal and apnea patients, and the
results were obtained after 10-fold-cross validation of the different machine
learning techniques viz., univariate regression, multivariate regression,
support vector machine and random forest. The best results in screening OSA
were obtained from random forest algorithm with the highest performance
(Acc: 98.0%, Sen: 98.6%, Spec: 99.3%) for all the combined features. The
proposed work is an effective system for automatic screening of OSA from a
single PPG sensor, thereby reducing the need for a very expensive and
overnight polysomnography sleep study.
Creator
Smily Jeya Jothi E., Anitha J., Jude Hemanth
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Mar 30, 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Smily Jeya Jothi E., Anitha J., Jude Hemanth, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques,” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/4116.
An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques,” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/4116.