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@article{ART002611230},
author={Seo-Yeong Kim and Young-Kyoon Suh},
title={A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2020},
volume={25},
number={7},
pages={113-123},
doi={10.9708/jksci.2020.25.07.113}

TY - JOUR
AU - Seo-Yeong Kim
AU - Young-Kyoon Suh
TI - A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research
JO - Journal of The Korea Society of Computer and Information
PY - 2020
VL - 25
IS - 7
PB - The Korean Society Of Computer And Information
SP - 113
EP - 123
SN - 1598-849X
AB - Obstructive sleep apnea (OSA) among sleep disorders is one of relatively common diseases. Patients can be checked for the disease through sleep polysomnography. However, as far as he diagnosis of OSA using polysomnography (PSG) is concerned, many practical problems such as an increasing number of patients, expensive testing cost, discomfort during examination, and the limited number of people for testing have been pointed out. Accordingly, for the purpose of substituting PSG researchers have been actively conducting studies on OSA diagnosis based on machine learning using bio signals. In this regard, we review a rich body of existing OSA diagnosis studies applying machine learning techniques based on bio-signal data. As a result, this paper presents a novel taxonomy of the reviewed studies and provides their comprehensive comparative analysis results. Also, we reveal various limitations of the studies using the bio signals and suggest several improvements about utilization of the used machine learning methods. Finally, this paper presents future research topics related to the application of machine learning techniques using bio signals.
KW - Obstructive Sleep Apnea;Polysomnography;Biosignal;Machine Learning;Feature Extraction
DO - 10.9708/jksci.2020.25.07.113
ER -

Seo-Yeong Kim and Young-Kyoon Suh. (2020). A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research. Journal of The Korea Society of Computer and Information, 25(7), 113-123.

Seo-Yeong Kim and Young-Kyoon Suh. 2020, "A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research", Journal of The Korea Society of Computer and Information, vol.25, no.7 pp.113-123. Available from: doi:10.9708/jksci.2020.25.07.113

Seo-Yeong Kim, Young-Kyoon Suh "A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research" Journal of The Korea Society of Computer and Information 25.7 pp.113-123 (2020) : 113.

Seo-Yeong Kim, Young-Kyoon Suh. A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research. 2020; 25(7), 113-123. Available from: doi:10.9708/jksci.2020.25.07.113

Seo-Yeong Kim and Young-Kyoon Suh. "A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research" Journal of The Korea Society of Computer and Information 25, no.7 (2020) : 113-123.doi: 10.9708/jksci.2020.25.07.113

Seo-Yeong Kim; Young-Kyoon Suh. A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research. Journal of The Korea Society of Computer and Information, 25(7), 113-123. doi: 10.9708/jksci.2020.25.07.113

Seo-Yeong Kim; Young-Kyoon Suh. A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research. Journal of The Korea Society of Computer and Information. 2020; 25(7) 113-123. doi: 10.9708/jksci.2020.25.07.113

Seo-Yeong Kim, Young-Kyoon Suh. A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research. 2020; 25(7), 113-123. Available from: doi:10.9708/jksci.2020.25.07.113

Seo-Yeong Kim and Young-Kyoon Suh. "A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research" Journal of The Korea Society of Computer and Information 25, no.7 (2020) : 113-123.doi: 10.9708/jksci.2020.25.07.113