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Study on the Detection of Vertigo Induced by GVS Based on EEG Signal Feature Binary Classification

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12th Asian-Pacific Conference on Medical and Biological Engineering (APCMBE 2023)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 104))

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Abstract

Objective: Galvanic Vestibular Stimulation (GVS) is a common method to induce reversible vertigo for the purpose of scientific or clinic study. Through the study of GVS induced vertigo, we can explore the working mechanism of vestibular nervous system. For the detection of GVS induced vertigo, we generally adopt the way of questionnaire survey of subjective feelings, which lacks objectivity. Therefore, this paper will classify GVS induced vertigo by EEG classification method to help determine GVS induced vertigo status. Methods: subjects were required to complete the Dizziness Handicap Inventory (DHI). The results were used as the data label for supervised Machine learning classification of EEG. We collected EEG signals before and after GVS stimulation. Then different sample features in EEG signals were extracted by short-time Fourier Transform, Sample Entropy (SampEn), Wavelet Transform and Ensemble Empirical Mode Decomposition (EEMD). Various machine learning classification models such as linear, nonlinear and neural network were used to classify and judge the state of vertigo. Results: The results showed that EEG classification on machine learning can realize the vertigo state detection. By comparing the classification results of various classification models under various data features, it is found that the AdaBoost nonlinear algorithm classification based on SampEn of EEG achieves the highest accuracy in the classification of whether there are vertigo, with an accuracy of 90.7% and an ROC curve area of 0.970. Conclusion: This paper obtained an ideal feature and classifier based on the feature classification of EEG signals in the detection of vertigo induced by GVS.

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Acknowledgment

This work is This work was supported in part by the National Natural Science Foundation of China under Grant “51877067”.

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Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Dingzigu, Hongqiao, Tianjin, 300130, China

    Y. Geng & W. Xue

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  1. Y. Geng
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  2. W. Xue
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Correspondence to Y. Geng .

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Editors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

    Guangzhi Wang

  2. School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China

    Dezhong Yao

  3. State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China

    Zhongze Gu

  4. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Yi Peng

  5. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanbao Tong

  6. State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Chengyu Liu

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Geng, Y., Xue, W. (2024). Study on the Detection of Vertigo Induced by GVS Based on EEG Signal Feature Binary Classification. In: Wang, G., Yao, D., Gu, Z., Peng, Y., Tong, S., Liu, C. (eds) 12th Asian-Pacific Conference on Medical and Biological Engineering. APCMBE 2023. IFMBE Proceedings, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-031-51485-2_44

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  • DOI: https://doi.org/10.1007/978-3-031-51485-2_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51484-5

  • Online ISBN: 978-3-031-51485-2

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