Automatic Sleep Stage Classification Using 1D Convolutional Neural Network

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Salamatian Asma; KHADEM ALI

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Journal Paper

Paper Information

Journal: Frontiers in Biomedical Technologies Year:2020 | Volume:7 | Issue:3 Page(s): 142-150

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Information Journal Paper

Title

Automatic Sleep Stage Classification Using 1D Convolutional Neural Network

Author(s)

Salamatian Asma | KHADEM ALI | Issue Writer Certificate

Keywords

Sleep Staging

1D Convolutional Neural Network

Classification

Electroencephalogram

Abstract

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage Classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, Classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient Sleep Staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal. Results: Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94. 09%, 74. 73%, 96. 43%, and 71. 02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method. Conclusion: In this study, a 13-layer 1D CNN model was proposed for sleep stage Classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and Classification stages. Having a low number of network parameters and layers while still having high Classification accuracy, is the main advantage of the proposed method over most of the previous deep learningbased approaches.

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APA: Copy

Salamatian, Asma, & KHADEM, ALI. (2020). Automatic Sleep Stage Classification Using 1D Convolutional Neural Network. FRONTIERS IN BIOMEDICAL TECHNOLOGIES, 7(3), 142-150. SID. https://sid.ir/paper/662474/en

Vancouver: Copy

Salamatian Asma, KHADEM ALI. Automatic Sleep Stage Classification Using 1D Convolutional Neural Network. FRONTIERS IN BIOMEDICAL TECHNOLOGIES[Internet]. 2020;7(3):142-150. Available from: https://sid.ir/paper/662474/en

IEEE: Copy

Asma Salamatian, and ALI KHADEM, “Automatic Sleep Stage Classification Using 1D Convolutional Neural Network,” FRONTIERS IN BIOMEDICAL TECHNOLOGIES, vol. 7, no. 3, pp. 142–150, 2020, [Online]. Available: https://sid.ir/paper/662474/en

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