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Epileptic focus localization using transfer learning on multi-modal EEG
Yang, Yong1,2,3; Li, Feng4; Luo, Jing4; Qin, Xiaolin2; Huang, Dong1
2023-11-23
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.
关键词epileptic focus localization multi-modal EEG transfer learning adversarial training patient-independent
DOI10.3389/fncom.2023.1294770
发表期刊FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷号17页码:10
通讯作者Yang, Yong(yangyong@cigit.ac.cn) ; Li, Feng(954036024@qq.com)
收录类别SCI
WOS记录号WOS:001115182900001
语种英语