Constantino Alexander C, Sisterson Nathaniel D, Zaher Naoir, Urban Alexandra, Richardson R Mark, Kokkinos Vasileios
Brain Modulation Lab, Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States.
Front Neurol. 2021 May 3;12:603868. doi: 10.3389/fneur.2021.603868. eCollection 2021.
Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS). Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed the area-under-precision-recall curve (AUPRC). In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72-0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3-8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68-0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8-8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features. We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.
癫痫手术中的决策与颅内脑电图（iEEG）的解读密切相关。尽管深度学习方法在处理颅外脑电图方面已证明具有高效性，但很少有研究涉及iEEG癫痫发作检测，部分原因是颅内检查通常每个患者可获得的癫痫发作次数较少。本研究旨在使用从植入响应性神经刺激系统（RNS）的癫痫患者收集的大量发作模式数据集，评估深度学习方法检测iEEG癫痫发作的效率。从22名植入RNS的患者中收集了5226次发作事件。创建了一个卷积神经网络（CNN）架构，为每个患者提供个性化的癫痫发作注释。在两种情况下测试癫痫发作识别的准确性：经过一段时间慢性记录后出现癫痫发作的患者（情况1）和植入后立即出现癫痫发作的患者（情况2）。将CNN识别RNS记录的iEEG发作模式的准确性与人类神经生理学专业知识进行比较。通过精确召回率曲线下面积（AUPRC）评估统计性能。在情况1中，CNN在30个种子样本时实现了最大平均二元分类AUPRC为0.84±0.19（95%CI，0.72 - 0.93），平均回归准确率为6.3±1.0秒（95%CI，4.3 - 8.5秒）。在情况2中，20个种子样本时最大平均AUPRC为0.80±0.19（95%CI，0.68 - 0.91），平均回归准确率为6.3±0.9秒（95%CI，4.8 - 8.3秒）。在两种情况下，种子大小为10时我们都获得了接近最大的准确率。CNN分类失败可由发作期电衰减、短暂发作、单通道发作模式、高度集中的发作间期活动、睡眠 - 觉醒周期变化以及脑电图发作特征的渐进调制来解释。我们开发了一种深度学习神经网络，能够以专家级准确性对RNS衍生的发作模式进行个性化检测。这些结果表明自动化技术有可能显著改善闭环脑刺激的管理，包括在记录初期，此时设备对特定患者的癫痫发作情况尚不了解。
