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EEGNet Classification of Two Channels Sleep EEG for IndividualSpecialization Based on Data Augmentation

Published: 29 May 2024 Publication History

Abstract

Abstract: With the continuous pursuit of personalized healthcare, personalized management of sleep health is increasingly gaining attention. This study aims to rapidly construct a personalized sleep electroencephalogram (EEG) classification model using a small amount of two channels sleep EEG data through data augmentation techniques. Leveraging Discrete Cosine Transform (DCT) technology, we propose an effective data augmentation method to enhance the diversity of a limited dataset and improve the robustness of the model. Through the training and optimization of a deep learning model, our focus is on achieving accurate predictions of individual sleep patterns, thereby providing a more refined solution for personalized sleep management. In this paper, we provide a detailed overview of the methodology, experimental results, and discussions, emphasizing the critical role of data augmentation in enhancing model performance and personalized sleep healthcare

References

[1]
Rajeev Agarwal and Jean Gotman. 2001. Computer-assisted sleep staging. IEEE Transactions on Biomedical Engineering 48, 12 (2001), 1412–1423.
[2]
N. Ahmed, T. Natarajan, and K.R. Rao. 1974. Discrete Cosine Transform. IEEE Trans. Comput. C-23, 1 (1974), 90–93. https://doi.org/10.1109/T-C.1974.223784
[3]
Josep Dinarès-Ferran, Rupert Ortner, Christoph Guger, and Jordi Solé-Casals. 2018. A new method to generate artificial frames using the empirical mode decomposition for an EEG-based motor imagery BCI. Frontiers in neuroscience 12 (2018), 308.
[4]
Rafael Elul. 1972. The genesis of the EEG. International review of neurobiology 15 (1972), 227–272.
[5]
Tsung-Hao Hsieh, Meng-Hsuan Liu, Chin-En Kuo, Yung-Hung Wang, and Sheng-Fu Liang. 2021. Home-use and real-time sleep-staging system based on eye masks and mobile devices with a deep learning model. Journal of medical and biological engineering 41 (2021), 659–668.
[6]
Conrad Iber. 2007. The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specification. (No Title) (2007).
[7]
Douglas Kirsch, R Benca, and A Eichler. 2015. Stages and architecture of normal sleep. UpToDate. Retrieved, March19 (2015), 2020–2020.
[8]
Abhay Koushik, Judith Amores, and Pattie Maes. 2019. Real-time smartphone-based sleep staging using 1-channel EEG. In 2019 IEEE 16th international conference on wearable and implantable body sensor networks (BSN). IEEE, 1–4.
[9]
Velayudhan Mohan Kumar. 2008. Sleep and sleep disorders. Indian Journal of Chest Diseases and Allied Sciences 50, 1 (2008), 129–129.
[10]
Vernon J Lawhern, Ari J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance. 2018. EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces. Journal of Neural Engineering 15, 5 (2018), 056013. https://doi.org/10.1088/1741-2552/aace8c arXiv:1803.04506
[11]
Diederik JF Nieuwenhuijs. 2006. Processed EEG in natural sleep. Best Practice & Research Clinical Anaesthesiology 20, 1 (2006), 49–56.
[12]
Thomas Penzel and Regina Conradt. 2000. Computer based sleep recording and analysis. Sleep medicine reviews 4, 2 (2000), 131–148.
[13]
Zhiwen Zhang, Feng Duan, Jordi Sole-Casals, Josep Dinares-Ferran, Andrzej Cichocki, Zhenglu Yang, and Zhe Sun. 2019. A novel deep learning approach with data augmentation to classify motor imagery signals. IEEE Access 7 (2019), 15945–15954.

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  • (2024)Modeling the two-dimensional variations in EEG signals to analyze the impact of music on sleep statesAlexandria Engineering Journal10.1016/j.aej.2024.09.053108(1-10)Online publication date: Dec-2024

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  1. EEGNet Classification of Two Channels Sleep EEG for IndividualSpecialization Based on Data Augmentation

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    cover image ACM Other conferences
    CACML '24: Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
    March 2024
    478 pages
    ISBN:9798400716416
    DOI:10.1145/3654823
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 29 May 2024

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    Author Tags

    1. Data Augmentation
    2. Deep learning
    3. EEG
    4. Model Classification
    5. Personalized Sleep Management

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    • JSPS KAKENHI

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    CACML 2024

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    Overall Acceptance Rate 93 of 241 submissions, 39%

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    • (2024)Modeling the two-dimensional variations in EEG signals to analyze the impact of music on sleep statesAlexandria Engineering Journal10.1016/j.aej.2024.09.053108(1-10)Online publication date: Dec-2024

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