Li Zan, Fields Madeline, Panov Fedor, Ghatan Saadi, Yener Bülent, Marcuse Lara
Department of Electrical, Computer, and Systems Engineering (ECSE), Rensselaer Polytechnic Institute, Troy, NY, United States.
Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Front Neurol. 2021 Nov 11;12:705119. doi: 10.3389/fneur.2021.705119. eCollection 2021.
In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.
在耐药性癫痫（DRE）患者中，癫痫发作不可预测，常常几乎没有或完全没有预警就发生。这种不可预测性导致了焦虑以及癫痫发作造成的许多发病率和死亡率。在这项研究中，对19例DRE患者的102次颞叶内侧起始的癫痫发作进行了分析，这些患者在手术评估过程中同时进行了颅内脑电图（iEEG）和头皮脑电图检查。本文的首要目标是开发癫痫发作预测和检测的机器学习模型：（i）仅使用iEEG，（ii）仅使用头皮脑电图，以及（iii）联合分析iEEG和头皮脑电图。第二个目标是测试当癫痫发作在肉眼无法检测到（表面阴性）但在iEEG中可见时，机器学习是否能够在头皮脑电图上检测到癫痫发作。最后一个问题是确定深度学习算法是否能够正确地对癫痫发作起始进行定位。通过在4个类别上训练深度神经网络（DNN）来共同解决癫痫发作检测和预测问题：非癫痫发作、癫痫发作前、左侧颞叶内侧起始癫痫发作和右侧颞叶内侧起始癫痫发作。为了实现这些目标，使用两个深度神经网络（DNN）针对3种不同类型的相似性图测试分类准确率，这些相似性图使用了不同的脑电图数据时间序列。具有韦克斯曼相似性图的卷积神经网络（CNN）在所有脑电图数据（iEEG、头皮脑电图和组合数据）中产生了最高的准确率。具体而言，脑电图的1秒时段在超过98%的时间里被正确地归类为癫痫发作、癫痫发作前或非癫痫发作类别。重要的是，在绝大多数时段（>97%）中，癫痫发作前状态被正确分类。仅从头皮脑电图数据中检测表面阴性癫痫发作以及具有延迟头皮起始（表面阴性部分）的癫痫发作的准确率超过97%。此外，该模型能够准确地对来自头皮数据的所有癫痫发作进行定位，包括表面阴性癫痫发作。这项研究表明，使用颅内或头皮脑电图数据进行高度准确的癫痫发作预测和检测是可行的。此外，即使表面阴性癫痫发作肉眼不可见，也可以通过机器学习准确地对其进行预测、检测和定位。
