Self-superviesed sleep staging model based on contrast learing

Huilong Ding; Dong Zhang; Kaiyuan Qi; Dong Wu

doi:10.1117/12.2684819

21 July 2023 Self-superviesed sleep staging model based on contrast learing

Huilong Ding, Dong Zhang, Kaiyuan Qi, Dong Wu

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171P (2023) https://doi.org/10.1117/12.2684819
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

In the method of sleep staging task, the artificial sleep staging process is complicated, while the traditional machine learning only realizes feature classification and relies on manual feature extraction. This paper presents a deep learning method which can automatically extract and classify EEG features. At the same time, in view of the slow improvement of staging accuracy and high model complexity of existing studies, and the high cost of data due to the dependence on annotated data sets, this paper constructs the end-to-end self-supervised sleep staging model for these problems and continuously improves it. An automatic sleep staging model based on Inception structure and BILSTM was proposed. The convolutional neural network of Inception structure extracts the corresponding features from different time scales, combines the application of attention mechanism and residual structure to carry out the EEG signal characterization learning, and uses BILSTM to carry out the sequence information feature learning. Finally, a self-supervised learning method based on comparison is used to learn the potential feature representation of EEG data from a large number of unlabeled data. In the experiment, the sleep-EDF dataset was used, and the results showed that self-supervision improved the overall classification accuracy of the model. Meanwhile, the classification results of various categories were equalized, and the overall performance of the Sleep staging model was improved.

Citation Download Citation

Huilong Ding, Dong Zhang, Kaiyuan Qi, and Dong Wu "Self-superviesed sleep staging model based on contrast learing", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171P (21 July 2023); https://doi.org/10.1117/12.2684819

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KEYWORDS

Machine learning

Data modeling

Performance modeling

Education and training

Electroencephalography

Feature extraction

Deep learning

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