Stirling Rachel E, Maturana Matias I, Karoly Philippa J, Nurse Ewan S, McCutcheon Kate, Grayden David B, Ringo Steven G, Heasman John M, Hoare Rohan J, Lai Alan, D'Souza Wendyl, Seneviratne Udaya, Seiderer Linda, McLean Karen J, Bulluss Kristian J, Murphy Michael, Brinkmann Benjamin H, Richardson Mark P, Freestone Dean R, Cook Mark J
Seer Medical Pty Ltd, Melbourne, VIC, Australia.
Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.
Front Neurol. 2021 Aug 23;12:713794. doi: 10.3389/fneur.2021.713794. eCollection 2021.
Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.
准确识别癫痫发作活动，包括临床发作和亚临床发作，对癫痫的管理具有重要意义。准确识别癫痫发作活动对于诊断、管理和预测目的至关重要，但患者报告的发作情况已被证明不可靠。早期研究表明，使用植入式颅内装置可以准确捕捉脑电图癫痫发作并进行预测，但侵入性较小的脑电图（EEG）记录系统将是最佳选择。在此，我们展示了使用一种微创头皮下装置连续记录脑电图进行癫痫发作检测和预测的初步结果。五名每月至少经历两次临床可识别癫痫发作的难治性癫痫患者被植入了头皮下装置（Minder），该装置从大脑的两个半球提供两个通道的数据。数据通过耳后系统持续采集，该系统也为装置供电，并无线传输到手机，然后通过云存储可远程访问。在为期1周的动态视频脑电图监测期间，将头皮下装置的脑电图记录与传统系统记录的数据进行了比较。使用机器学习算法检测可疑癫痫样活动（EA），并由训练有素的神经生理学家进行审查。通过利用EA中的周期和先前的癫痫发作时间进行回顾性癫痫发作预测。这些程序和装置耐受性良好，未报告重大并发症。在头皮下系统上准确识别出癫痫发作，同时期传统头皮脑电图记录的时间段在视觉上证实了这一点。所获取的数据也使得能够成功进行癫痫发作预测。获得的受试者操作特征曲线下面积（AUC评分）为0.88，这与近期使用颅内脑电图进行的最先进预测工作中的最佳评分相当。
