A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram

Chengfan Li, Yueyu Qi, Xuehai Ding, Junjuan Zhao, Tian Sang and Matthew Lee
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Chengfan Li: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Yueyu Qi: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Xuehai Ding: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Junjuan Zhao: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Tian Sang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Matthew Lee: 12th Grade, The Bishop’s School, La Jolla, CA 92037, USA

IJERPH, 2022, vol. 19, issue 10, 1-17

Abstract: The classification of sleep stages is an important process. However, this process is time-consuming, subjective, and error-prone. Many automated classification methods use electroencephalogram (EEG) signals for classification. These methods do not classify well enough and perform poorly in the N1 due to unbalanced data. In this paper, we propose a sleep stage classification method using EEG spectrogram. We have designed a deep learning model called EEGSNet based on multi-layer convolutional neural networks (CNNs) to extract time and frequency features from the EEG spectrogram, and two-layer bi-directional long short-term memory networks (Bi-LSTMs) to learn the transition rules between features from adjacent epochs and to perform the classification of sleep stages. In addition, to improve the generalization ability of the model, we have used Gaussian error linear units (GELUs) as the activation function of CNN. The proposed method was evaluated by four public databases, the Sleep-EDFX-8, Sleep-EDFX-20, Sleep-EDFX-78, and SHHS. The accuracy of the method is 94.17%, 86.82%, 83.02% and 85.12%, respectively, for the four datasets, the MF1 is 87.78%, 81.57%, 77.26% and 78.54%, respectively, and the Kappa is 0.91, 0.82, 0.77 and 0.79, respectively. In addition, our proposed method achieved better classification results on N1, with an F1-score of 70.16%, 52.41%, 50.03% and 47.26% for the four datasets.

Keywords: sleep stage classification; electroencephalogram spectrogram; deep learning; convolutional neural network; long short-term memory network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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