作者
Henri Korkalainen, Juhani Aakko, Sami Nikkonen, Samu Kainulainen, Akseli Leino, Brett Duce, Isaac O Afara, Sami Myllymaa, Juha Töyräs, Timo Leppänen
发表日期
2019/12/19
期刊
IEEE journal of biomedical and health informatics
卷号
24
期号
7
页码范围
2073-2081
出版商
IEEE
简介
The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with suspected OSA were used to develop a combined convolutional and long short-term memory neural network. On the public dataset, the model achieved sleep staging accuracy of 83.7% (κ = 0.77) with a single frontal EEG channel and 83.9% (κ = 0.78) when supplemented with EOG. For the clinical dataset, the model …
引用总数
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H Korkalainen, J Aakko, S Nikkonen, S Kainulainen… - IEEE journal of biomedical and health informatics, 2019