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.

中文翻译：

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使用多模态脑电图迁移学习进行癫痫病灶定位

癫痫的标准治疗方法是药物治疗和手术切除。然而，约1/3的顽固性癫痫患者具有耐药性，需要手术切除癫痫病灶。为了解决耐药性癫痫病灶定位问题，我们提出了一种多模态脑电图（iEEG 和 sEEG）的迁移学习方法。采用 10 倍交叉验证方法在 Bern-Barcelona 和 Bonn 数据集上验证预训练模型的性能，准确率分别达到 94.50 和 97.50%。实验结果表明，预训练模型在准确性、灵敏度和阴性预测值方面优于竞争性的最先进基线。此外，我们使用重庆医科大学的癫痫数据集对预训练模型进行了微调，并使用留一交叉验证方法对其进行了测试，获得了令人印象深刻的 90.15% 的平均准确率。该方法显示了癫痫通道和非癫痫通道之间的显着特征差异。通过使用神经网络提取数据特征，可以实现癫痫通道和非癫痫通道的准确分类。因此，该模型的优越性能表明该方法对于癫痫病灶定位非常有效，可以帮助医生进行临床定位诊断。