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A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence

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Abstract

With the development of telemedicine and edge computing, edge artificial intelligence (AI) will become a new development trend for smart medicine. On the other hand, nearly one-third of children suffer from sleep disorders. However, all existing sleep staging methods are for adults. Therefore, we adapted edge AI to develop a lightweight automatic sleep staging method for children using single-channel EEG. The trained sleep staging model will be deployed to edge smart devices so that the sleep staging can be implemented on edge devices which will greatly save network resources and improving the performance and privacy of sleep staging application. Then the results and hypnogram will be uploaded to the cloud server for further analysis by the physicians to get sleep disease diagnosis reports and treatment opinions. We utilized 1D convolutional neural networks (1D-CNN) and long short term memory (LSTM) to build our sleep staging model, named CSleepNet. We tested the model on our childrens sleep (CS) dataset and sleep-EDFX dataset. For the CS dataset, we experimented with F4-M1 channel EEG using four different loss functions, and the logcosh performed best with overall accuracy of 83.06% and F1-score of 76.50%. We used Fpz-Cz and Pz-Oz channel EEG to train our model in Sleep-EDFX dataset, and achieved an accuracy of 86.41% without manual feature extraction. The experimental results show that our method has great potential. It not only plays an important role in sleep-related research, but also can be widely used in the classification of other time sequences physiological signals.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62172340, in part by the Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX0041, in part by the Young and Middle-aged Senior Medical Talents Studio of Chongqing under grant ZQNYXGDRCGZS2021002, and in part by the Introduced Talent Program of Southwest University under Grant SWU020008. The corresponding author is Yuan Zhang.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Liqiang Zhu & Yuan Zhang

  2. Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

    Changming Wang

  3. Brain-inspired Intelligence and Clinical Translational Research Center, Beijing, 100176, China

    Changming Wang

  4. Department of Pediatric Respiration, Chongqing Ninth People’s Hospital, Chongqing, 400700, China

    Zhihui He

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  1. Liqiang Zhu
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  2. Changming Wang
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Correspondence to Yuan Zhang.

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Liqiang Zhu and Changming Wang contributed equally to this work.

This article belongs to the Topical Collection: Special Issue on Resource Management at the Edge for Future Web, Mobile and IoT Applications

Guest Editors: Qiang He, Fang Dong, Chenshu Wu, and Yun Yang

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Zhu, L., Wang, C., He, Z. et al. A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence. World Wide Web 25, 1883–1903 (2022). https://doi.org/10.1007/s11280-021-00983-3

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  • DOI: https://doi.org/10.1007/s11280-021-00983-3

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