Objective. To identify a new electrophysiological feature characterising the epileptic seizures, which is commonly observed in different types of epilepsy. Methods. We recorded the intracranial electroencephalogram (iEEG) of 21 patients (12 women and 9 men) with multiple types of refractory epilepsy. The raw iEEG signals of the early phase of epileptic seizures and interictal states were classified by a convolutional neural network (Epi-Net). For comparison, the same signals were classified by a support vector machine (SVM) using the spectral power and phase–amplitude coupling. The features learned by Epi-Net were derived by a modified integrated gradients method. We considered the product of powers multiplied by the relative contribution of each frequency amplitude as a data-driven epileptogenicity index (d-EI). We compared the d-EI and other conventional features in terms of accuracy to detect the epileptic seizures. Finally, we compared the d-EI among the electrodes to evaluate its relationship with the resected area and the Engel classification. Results. Epi-Net successfully identified the epileptic seizures, with an area under the receiver operating characteristic curve of 0.944 0.067, which was significantly larger than that of the SVM (0.808 0.253, n =21; p =0.025). The learned iEEG signals were characterised by increased powers of 17–92 Hz and >180 Hz in addition to decreased powers of other frequencies. The proposed d-EI detected them with better accuracy than the other iEEG features. Moreover, the surgical resection of areas with a larger increase in d-EI was observed for all nine patients with Engel class ⩽1, but not for the 4 of 12 patients with Engel class >1, demonstrating the significant association with seizure outcomes. Significance. We derived an iEEG feature from the trained Epi-Net, which identified the epileptic seizures with improved accuracy and might contribute to identification of the epileptogenic zone.

目标。确定表征癫痫发作的新电生理特征，这在不同类型的癫痫中很常见。方法. 我们记录了 21 名患有多种类型难治性癫痫的患者（12 名女性和 9 名男性）的颅内脑电图 (iEEG)。癫痫发作早期和发作间期的原始 iEEG 信号通过卷积神经网络 (Epi-Net) 进行分类。为了比较，相同的信号通过支持向量机 (SVM) 使用频谱功率和相位-幅度耦合进行分类。Epi-Net 学习的特征是通过改进的集成梯度方法导出的。我们将功率乘以每个频率幅度的相对贡献的乘积作为数据驱动的致癫痫指数 (d-EI)。我们在检测癫痫发作的准确性方面比较了 d-EI 和其他传统特征。最后，结果。Epi-Net 成功识别癫痫发作，受试者操作特征曲线下面积为 0.944 0.067，明显大于 SVM（0.808 0.253，n = 21；p = 0.025）。除了其他频率的功率降低外，学习到的 iEEG 信号的特征在于 17-92 Hz 和 >180 Hz 的功率增加。所提出的 d-EI 比其他 iEEG 特征更准确地检测到它们。此外，在所有 9 名 Engel 分级 ⩽1 患者中都观察到了 d-EI 增加较大的区域的手术切除，但在 12 名 Engel 分级 >1 患者中的 4 名未观察到，这表明与癫痫发作结果显着相关。意义。 我们从训练有素的 Epi-Net 中获得了一个 iEEG 特征，该特征以更高的准确度识别癫痫发作，并可能有助于识别癫痫发生区。