基于多频段图模型的微弱脑电信号异常检测
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TP391

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山东省自然科学基金项目(ZR2019MEE063)


Abnormal Detection of Weak EEG Signal Based on Multi-frequency Wave Graph Model
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The Shandong Provincial Natural Science Foundation, China (ZR2019MEE063)

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    摘要:

    脑电信号(electroencephalogram,EEG)在癫痫发作检测方面具有重要意义。为了实现对癫痫发作的早期预警,充分利用δ,θ,α,β和 γ波这五个频段中脑电的微弱变化信息和图模型的独特优势,提出了基于多频段图模型的脑电信号微弱异常变化检测方法。该方法首先对滤波后脑电信号的5个频段分别进行图模型动态建模,利用距离函数得到量化图模型之间关系的相似性分数,并用自适应权重融合算法融合所有的相似性分数得到综合性指标,最终通过假设检验来判断脑电信号是否发生异常。利用公开的波士顿儿童医院-麻省理工学院(Children’s Hospital Boston - Massachusetts Institute of Technology, CHB-MIT)头皮脑电信号数据库和山东大学第二医院神经内科的EEG数据库分别进行了实验,并最终用查准率、查全率和F分数来评价所提方法的检测性能。通过与基准方法比较,实验结果表明:所提方法在查准率和F分数方面优于基准方法,且查全率结果可达100%,表明所提方法能够检测所有潜在的微弱脑电信号异常变化,实现了对所有癫痫发作时刻的变化检测,具有突出的优越性和广阔的应用潜力。

    Abstract:

    Electroencephalogram (EEG) signals is significant in dealing with seizure detection. In order to achieve early warning of seizures, weak change information of EEG in the five frequency bands of δ, θ, α, β and γ waves and the unique advantages of the graph model were fully utilized. A method for weak abnormal change detection of EEG signal based on multi-frequency graph model was proposed. Firstly, dynamic modeling of graph models on the five frequency waves of the filtered EEG signal was performed in the proposed method, and the distance function was used to quantify the similarity scores between the graph models. Then comprehensive indicators were obtained by all the similarity scores with an adaptive weighed fusion algorithm. A common null hypothesis test was finally employed for detecting whether the state of EEG signals was abnormal. The public CHB-MIT scalp EEG database and the EEG database of the Department of Neurology of the Second Hospital of Shandong University were used to conduct experiments, and precision, recall and F-score was evaluated the detection performance of the proposed method. Compared with the benchmark methods, the experimental results show that the proposed method is superior to the benchmark method in terms of precision and F-score, and the recall is 100%, it is show that the proposed method is able to detect all potential abnormal changes in weak EEG signals, achieving outstanding superiority and broad application potential for the detection of changes in all seizure moments.

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贺光硕,卢国梁,尚伟. 基于多频段图模型的微弱脑电信号异常检测[J]. 科学技术与工程, 2022, 22(24): 10638-10645.
He Guangshuo, Lu Guoliang, Shang Wei. Abnormal Detection of Weak EEG Signal Based on Multi-frequency Wave Graph Model[J]. Science Technology and Engineering,2022,22(24):10638-10645.

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历史
  • 收稿日期:2021-12-02
  • 最后修改日期:2022-05-21
  • 录用日期:2022-04-04
  • 在线发布日期: 2022-09-08
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