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A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(7), pp.113-123
  • DOI : 10.9708/jksci.2020.25.07.113
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 22, 2020
  • Accepted : June 19, 2020
  • Published : July 31, 2020

Seo-Yeong Kim 1 Young-Kyoon Suh 1

1경북대학교

Accredited

ABSTRACT

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.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.