Epileptic focus localization using transfer learning on multi-modal EEG

作者全名："Yang, Yong; Li, Feng; Luo, Jing; Qin, Xiaolin; Huang, Dong"

作者地址："[Yang, Yong; Huang, Dong] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China; [Yang, Yong; Qin, Xiaolin] Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Peoples R China; [Yang, Yong] Univ Chinese Acad Sci, Chongqing Sch, Chongqing, Peoples R China; [Li, Feng; Luo, Jing] 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, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China.; Yang, Y (通讯作者)，Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Peoples R China.; Yang, Y (通讯作者)，Univ Chinese Acad Sci, Chongqing Sch, Chongqing, Peoples R China.; Li, F (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Neurol, Chongqing, Peoples R China."

来源：FRONTIERS IN COMPUTATIONAL NEUROSCIENCE

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001115182900001

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：epileptic focus localization; multi-modal EEG; transfer learning; adversarial training; patient-independent

摘要："The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied to validate the performance of the pre-trained model on the Bern-Barcelona and Bonn datasets, achieving accuracy rates of 94.50 and 97.50%, respectively. The experimental results have demonstrated that the pre-trained model outperforms the competitive state-of-the-art baselines in terms of accuracy, sensitivity, and negative predictive value. Furthermore, we fine-tuned our pre-trained model using the epilepsy dataset from Chongqing Medical University and tested it using the leave-one-out cross-validation method, obtaining an impressive average accuracy of 90.15%. This method shows significant feature differences between epileptic and non-epileptic channels. By extracting data features using neural networks, accurate classification of epileptic and non-epileptic channels can be achieved. Therefore, the superior performance of the model has demonstrated that the proposed method is highly effective for localizing epileptic focus and can aid physicians in clinical localization diagnosis."

基金机构：Sichuan Science and Technology Plan of China

基金资助正文：No Statement Available