Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment efficacy evaluation. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals. This pipeline integrates: a new pre-processing technique which segments continuous raw EEG signals without prior distinction between seizure and seizure-free activities; a post-processing algorithm developed to reassemble EEG segments and allow the identification of seizures start/end; and finally, a new evaluation procedure based on a strict seizure events comparison between predicted and real labels. Models training have been performed using a data splitting strategy which addresses the potential for data leakage. We demonstrated the fundamental differences between a seizure classification and a seizure detection task and showed the differences in performance between the two tasks. Finally, we demonstrated the generalization capabilities across species of our best architecture, combining a Convolutional Neural Network and a Transformer encoder. The model was trained on animal EEGs and tested on human EEGs with a F1-score of 93% on a balanced Bonn dataset.

翻译：癫痫是全球最为普遍的神经系统疾病。约三分之一的颞叶内侧癫痫（MTLE）患者表现出药物抵抗性，这迫切需要开发新的治疗方法。在抗癫痫药物（ASM）研发中，关键环节是检测和量化脑电图（EEG）信号中出现的癫痫发作，这对于评估治疗效果至关重要。本研究提出了一种基于深度学习模型的癫痫发作检测流程，应用于原始脑电信号。该流程整合了：一种新的预处理技术，可在不预先区分发作期与非发作期活动的情况下对连续原始脑电信号进行分段；一种用于重组脑电信号段以识别发作起始/终止的后处理算法；以及基于预测标签与真实标签间严格发作事件对比的新评估流程。模型训练采用可避免数据泄露潜在风险的数据划分策略。我们论证了癫痫发作分类任务与发作检测任务之间的本质差异，并展示了两者在性能上的区别。最后，我们证明了最佳架构（结合卷积神经网络与Transformer编码器）具备跨物种泛化能力。该模型在动物脑电信号上训练，并在人类脑电信号上测试，在平衡的Bonn数据集上取得了93%的F1分数。