기계 학습 기반 폐쇄성 수면 무호흡증 진단 연구에 대한 문헌 조사 = A Literature Survey of Machine Learning Based Obstructive Sleep Apnea Diagnosis Research

다국어 초록 (Multilingual 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.

국문 초록 (Abstract)

수면 장애 중 폐쇄성수면무호흡증은 비교적 흔한 질병 중 하나이다. 환자들은 수면다원검사를통해 해당 질환의 여부를 알아볼 수 있다. 그러나 수면다원검사를 이용한 폐쇄성수면무호흡증 진단에 관한 한, 늘어나는 환자 수, 비싼 검사 비용, 검사 중 불편함, 수용 인원 제한 등 현실적인문제점들이 지적됐다. 이에 따라, 수면다원검사를 대체할 목적으로 연구자들은 생체 신호를 활용한 기계학습 기반 폐쇄성수면무호흡증 진단 연구들을 활발히 진행해 왔다. 이 시점에서, 우리는생체 신호 데이터를 기반으로 기계학습 기법을 적용하는 폐쇄성수면무호흡증 진단 연구를 복기한다. 그 결과, 본 논문은 복기 된 연구들에 대한 최신 분류 체계를 제시하고 그 연구들의 종합적인비교 분석 결과를 제공한다. 또한, 본 논문은 생체 신호를 활용한 연구들의 다양한 한계점을 밝히고 사용된 기계학습 기법의 활용성에 대한 여러 개선점을 제안한다. 끝으로, 본 논문은 생체 신호를 활용한 기계학습 기법 적용과 관련한 향후 연구 주제를 제시한다.

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