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EEG signal classification of tinnitus based on SVM and sample entropy

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Listed:
  • Mai Jianbiao
  • Wang Xinzui
  • Li Zhaobo
  • Liu Juan
  • Zhang Zhongwei
  • Fu Hui

Abstract

The prevalence of tinnitus is high and seriously affects the daily life of patients. As the pathogenesis of tinnitus is not yet clear, there is a lack of rapid and objective diagnostic modalities. In order to provide clinicians with an objective diagnostic approach, this paper combines time-frequency domain and non-linear power analysis to investigate the differences in the specificity of the EEG signal in tinnitus patients compared to healthy subjects. In this paper, resting-state electroencephalograms (EEG) were collected from 10 cases each of tinnitus patients and healthy subjects, and the data from the two groups were compared in the δ (0.5 − 3 .5 Hz), θ (4 − 7.5 Hz), α1 (8 − 10 Hz), α2 (10 − 12 Hz), β1 (13 − 18 Hz), β2 (18.5 − 21 Hz), β3 (21.5 − 30 Hz), and γ (30.5 − 44 Hz) bands for the differences in sample entropy values. The results of the resting state experiment revealed that the δ, α2 and β1 band samples of tinnitus patients all had greater entropy values than healthy subjects, with extremely significant differences compared to healthy subjects (p

Suggested Citation

  • Mai Jianbiao & Wang Xinzui & Li Zhaobo & Liu Juan & Zhang Zhongwei & Fu Hui, 2023. "EEG signal classification of tinnitus based on SVM and sample entropy," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 26(5), pages 580-594, April.
    • Handle: RePEc:taf:gcmbxx:v:26:y:2023:i:5:p:580-594
    DOI: 10.1080/10255842.2022.2075698
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