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Fast Convolutional Method for Automatic Sleep Stage Classification

OBJECTIVES: Polysomnography is essential to diagnose sleep disorders. It is used to identify a patient's sleep pattern during sleep. This pattern is obtained by a doctor or health practitioner by using a scoring process, which is time consuming. To overcome this problem, we developed a system t...

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Autores principales: Yulita, Intan Nurma, Fanany, Mohamad Ivan, Arymurthy, Aniati Murni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Medical Informatics 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6085207/
https://www.ncbi.nlm.nih.gov/pubmed/30109150
http://dx.doi.org/10.4258/hir.2018.24.3.170
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author Yulita, Intan Nurma
Fanany, Mohamad Ivan
Arymurthy, Aniati Murni
author_facet Yulita, Intan Nurma
Fanany, Mohamad Ivan
Arymurthy, Aniati Murni
author_sort Yulita, Intan Nurma
collection PubMed
description OBJECTIVES: Polysomnography is essential to diagnose sleep disorders. It is used to identify a patient's sleep pattern during sleep. This pattern is obtained by a doctor or health practitioner by using a scoring process, which is time consuming. To overcome this problem, we developed a system that can automatically classify sleep stages. METHODS: This paper proposes a new method for sleep stage classification, called the fast convolutional method. The proposed method was evaluated against two sleep datasets. The first dataset was obtained from physionet.org, a physiologic signals data centers. Twenty-five patients who had a sleep disorder participated in this data collection. The second dataset was collected in Mitra Keluarga Kemayoran Hospital, Indonesia. Data was recorded from ten healthy respondents. RESULTS: The proposed method reached 73.50% and 56.32% of the F-measures for the PhysioNet and Mitra Keluarga Kemayoran Hospital data, respectively. Both values were the highest among all the machine learning methods considered in this study. The proposed method also had an efficient running time. The fast convolutional models of the PhysioNet and Mitra Keluarga Kemayoran Hospital data needed 42.60 and 0.06 seconds, respectively. CONCLUSIONS: The fast convolutional method worked well on the tested datasets. It achieved a high F-measure result and an efficient running time. Thus, it can be considered a promising tool for sleep stage classification.
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spelling pubmed-60852072018-08-14 Fast Convolutional Method for Automatic Sleep Stage Classification Yulita, Intan Nurma Fanany, Mohamad Ivan Arymurthy, Aniati Murni Healthc Inform Res Original Article OBJECTIVES: Polysomnography is essential to diagnose sleep disorders. It is used to identify a patient's sleep pattern during sleep. This pattern is obtained by a doctor or health practitioner by using a scoring process, which is time consuming. To overcome this problem, we developed a system that can automatically classify sleep stages. METHODS: This paper proposes a new method for sleep stage classification, called the fast convolutional method. The proposed method was evaluated against two sleep datasets. The first dataset was obtained from physionet.org, a physiologic signals data centers. Twenty-five patients who had a sleep disorder participated in this data collection. The second dataset was collected in Mitra Keluarga Kemayoran Hospital, Indonesia. Data was recorded from ten healthy respondents. RESULTS: The proposed method reached 73.50% and 56.32% of the F-measures for the PhysioNet and Mitra Keluarga Kemayoran Hospital data, respectively. Both values were the highest among all the machine learning methods considered in this study. The proposed method also had an efficient running time. The fast convolutional models of the PhysioNet and Mitra Keluarga Kemayoran Hospital data needed 42.60 and 0.06 seconds, respectively. CONCLUSIONS: The fast convolutional method worked well on the tested datasets. It achieved a high F-measure result and an efficient running time. Thus, it can be considered a promising tool for sleep stage classification. Korean Society of Medical Informatics 2018-07 2018-07-31 /pmc/articles/PMC6085207/ /pubmed/30109150 http://dx.doi.org/10.4258/hir.2018.24.3.170 Text en © 2018 The Korean Society of Medical Informatics http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Yulita, Intan Nurma
Fanany, Mohamad Ivan
Arymurthy, Aniati Murni
Fast Convolutional Method for Automatic Sleep Stage Classification
title Fast Convolutional Method for Automatic Sleep Stage Classification
title_full Fast Convolutional Method for Automatic Sleep Stage Classification
title_fullStr Fast Convolutional Method for Automatic Sleep Stage Classification
title_full_unstemmed Fast Convolutional Method for Automatic Sleep Stage Classification
title_short Fast Convolutional Method for Automatic Sleep Stage Classification
title_sort fast convolutional method for automatic sleep stage classification
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6085207/
https://www.ncbi.nlm.nih.gov/pubmed/30109150
http://dx.doi.org/10.4258/hir.2018.24.3.170
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