基于脑电多特征融合的癫痫发作预测方法
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杭州电子科技大学

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TP181

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国家自然科学基金(批准号:61971168、61871427),之江实验室开放项目(批准号:2021MC0AB04)


A seizure prediction method based on EEG multi-feature fusion
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Hangzhou Danzi University

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    摘要:

    癫痫的发作会给患者的身体和精神造成极大的创伤,对癫痫发作的准确预测可以及时协助医生对患者采取治疗措施。为了准确预测癫痫发作,本文提出了脑电特征和多通道脑电交互特征相融合的癫痫发作预测方法。首先提出多尺度符号化排列传递熵对多通道脑电信号交互信息进行分析,生成同步矩阵,并通过显著性分析筛选癫痫发作相关的重要脑电通道,减少不必要特征对分类的干扰。其次对筛选通道后的脑电信号生成表征脑电信号特征的功率谱密度能量图(PSDED),和描述脑通道交互特征的同步矩阵图(SMD);将两个特征图融合,采用深度卷积神经网络(DCNN)对癫痫患者脑电信号进行分类识别,提高了学习能力和泛化能力,分类准确率可以达到96.825%;最后在分类的基础上采用预测评价系统,对癫痫发作预测性能进行评估,癫痫发作预测范围(SPH)为10分钟和发作发生期(SOP)为10分钟时,预测敏感性达到96.66%,误检率可以达到0.03/h;当SPH为30分钟,SOP为10分钟时,预测敏感性达到93.17%,误检率可以达到0.05/h。与现有研究结果比较,本文方法具有较好的预测敏感度和较低的误检率。

    Abstract:

    Seizures can cause great physical and mental trauma to patients, and accurate prediction of seizures can assist physicians to take treatment measures for patients in a timely manner. In order to accurately predict seizures, this paper proposes a seizure prediction method that integrates EEG features and multichannel EEG interaction features. Firstly, the multi-scale symbolic alignment transfer entropy is proposed to analyze the multi-channel EEG signal interaction information, generate the synchronization matrix, and screen the important EEG channels related to seizures by significance analysis to reduce the interference of unnecessary features to the classification. Next, the power spectral density energy diagram (PSDED), which characterizes the EEG signal, and the synchronization matrix diagram (SMD), which describes the interaction characteristics of brain channels, were generated for the EEG signals after screening the channels; the two feature maps were fused, and deep convolutional neural network (DCNN) was used to classify and identify the EEG signals of epilepsy patients, which improved the learning ability and generalization ability, and the classification accuracy could reach 96.825%; finally, a prediction evaluation system was used on the basis of classification to evaluate the seizure prediction performance, and the prediction sensitivity reached 96.66% and the false detection rate could reach 0.03/h when the seizure prediction range (SPH) was 10 minutes and the seizure onset period (SOP) was 10 minutes.When the SPH is 30 min and SOP is 10 min, the prediction sensitivity reaches 93.17% and the false detection rate can reach 0.05/h. Compared with the results of existing studies, the method in this paper has better prediction sensitivity and lower false detection rate.

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  • 收稿日期:2021-03-09
  • 最后修改日期:2021-09-30
  • 录用日期:2021-10-09
  • 在线发布日期: 2021-11-01
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