Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction

作者全名："Yang, Yong; Qin, Xiaolin; Wen, Han; Li, Feng; Lin, Xiaoguang"

作者地址："[Yang, Yong; Qin, Xiaolin; Wen, Han] Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Sichuan, Peoples R China; [Yang, Yong; Lin, Xiaoguang] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China; [Yang, Yong; Lin, Xiaoguang] Univ Chinese Acad Sci, Chongqing Sch, Chongqing, Peoples R China; [Li, Feng] Chongqing Med Univ, Affiliated Hosp 1, Dept Neurol, Chongqing, Peoples R China"

通信作者："Yang, Y (通讯作者)，Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Sichuan, Peoples R China.; Yang, Y (通讯作者)，Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China.; Yang, Y (通讯作者)，Univ Chinese Acad Sci, Chongqing Sch, Chongqing, Peoples R China."

来源：FRONTIERS IN COMPUTATIONAL NEUROSCIENCE

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:000990169100001

JCR分区：Q2

影响因子：2.1

年份：2023

卷号：17

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：seizure prediction; EEG; ECG; data fusion; adversarial training

摘要："Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction."

基金机构："Sichuan Science and Technology Plan of China [2019ZDZX0005, 2019ZDZX0006, 2020YFQ0056]"

基金资助正文："Funding This research was supported by Sichuan Science and Technology Plan of China Grants (2019ZDZX0005, 2019ZDZX0006, and 2020YFQ0056)."