Healthc Inform Res.  2018 Jul;24(3):170-178. 10.4258/hir.2018.24.3.170.

Fast Convolutional Method for Automatic Sleep Stage Classification

• Yulita IN
• Fanany MI
• Arymurthy

• KMID: 2418167
• DOI: http://doi.org/10.4258/hir.2018.24.3.170

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.

Keyword

Machine Learning; Neural Networks; Classification; Sleep Stages; Polysomnography

MeSH Terms

Classification*
Data Collection
Dataset
Humans
Indonesia
Machine Learning
Methods*
Polysomnography
Running
Sleep Stages*
Sleep Wake Disorders
Surveys and Questionnaires

Figure

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  Figure 1 Design of sleep stage classification.

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  Figure 2 Example polysomnography signals. The first, second, and third signals are electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG), respectively.

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  Figure 3 Architecture of the fast convolutional method.

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  Figure 4 One-dimensional convolution layer.

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  Figure 5 Binary tree of hierarchical softmax.

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