Fast Convolutional Method for Automatic Sleep Stage Classification.
10.4258/hir.2018.24.3.170
- Author:
Intan Nurma YULITA
1
;
Mohamad Ivan FANANY
;
Aniati Murni ARYMURTHY
Author Information
1. Machine Learning and Computer Vision (MLCV) Lab, Faculty of Computer Science, Universitas Indonesia, Jawa Barat, Indonesia. intanurma@gmail.com
- Publication Type:Original Article
- Keywords:
Machine Learning;
Neural Networks;
Classification;
Sleep Stages;
Polysomnography
- MeSH:
Classification*;
Data Collection;
Dataset;
Humans;
Indonesia;
Machine Learning;
Methods*;
Polysomnography;
Running;
Sleep Stages*;
Sleep Wake Disorders;
Surveys and Questionnaires
- From:Healthcare Informatics Research
2018;24(3):170-178
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
