OBJECTIVE Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings. METHODS An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation. RESULTS On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation. CONCLUSIONS Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes. SIGNIFICANCE Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

中文翻译：

---

使用深度学习检测颅内电极上的内侧颞叶癫痫样放电


目的 开发一种高性能算法来检测颅内电极记录上的内侧颞叶 (mTL) 癫痫样放电。方法 一位癫痫学家对 46 名癫痫患者的颅内脑电图记录数据集中的 13,959 个癫痫样放电进行了注释。使用该数据集，我们训练了一个卷积神经网络（CNN）来识别来自单个颅内双极通道的 mTL 癫痫样放电。对来自多个双极通道输入的 CNN 输出进行平均以生成最终的检测器输出。使用嵌套 5 倍交叉验证来估计算法性能。结果 在受试者工作特征曲线上，我们的算法实现了 0.996 的曲线下面积 (AUC) 和 0.981 的部分 AUC（特异性 > 0.9）。精确率-召回率曲线上的 AUC 为 0.807。在每分钟 1 个假阳性率下，灵敏度达到 84%。 35.9% 的误报检测对应于专家注释期间遗漏的癫痫样放电。结论 利用深度学习，我们开发了一种高性能、患者非特异性算法，用于检测颅内电极上的 mTL 癫痫样放电。意义 我们的算法具有许多潜在的应用，可用于了解 mTL 癫痫样放电对癫痫和认知的影响，以及开发专门减少 mTL 癫痫样活动的疗法。