A multimodal approach towards epileptic seizure detection and prediction

癫痫发作检测和预测的多模式方法

• 批准号: 436477205
• 负责人: Professorin Dr. Solveig Vieluf
• 金额: --
• 依托单位: Medizinische Klinik und Poliklinik I (Kardiologie) - Campus Großhadern
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/436477205?language=en
• 关键词: multimodal; approach; towards; epileptic; seizure

Epilepsy is one of the most common neurological diseases with far-reaching consequences for patients. Patients describe the uncertainty about when an epileptic seizure will occur as the greatest burden in their everyday lives. The disease itself results in recurrent epileptic seizures, which originate from excessive electrical discharges from groups of neurons and have been shown to alter the activity of the autonomic nervous system (ANS). Multimodal wristbands offer a non-invasive and easy-to-use possibility to continuously monitor ANS functioning. However, biomarkers and seizure prediction algorithm still lack specificity due to the high inter- and intra-individual variability present in ANS activity. The overall goal of my project is to identify biomarkers for seizure susceptibility and to develop a seizure prediction algorithm. I hypothesize that seizure-induced alterations of central activity, which show themselves within and across subsystems of the ANS, follow a multimodal pattern. Related to hyper-excitability of the brain in pre-ictal phases, which potentially alters the central drive of the subsystems of the ANS, such as cardiac, respiratory, and electrodermal system, I expect multimodal markers that express the amount of information shared between subsystems to increase.To analyze multimodal ANS changes, I will analyze recordings of continuous heart rate, respiratory rate, electrodermal activity and peripheral body temperature measured at the wrist while seizures were recorded and classified based on continuous video-EEG recording. To test theory-inspired biomarkers I will characterize the unimodal signals information content by use of entropy measures within subsystems and the information exchange by use of mutual information measures. To develop a seizure prediction algorithm that can identify high risk for seizure in ANS data. By use of uni- and multimodal biomarkers that indicate seizure susceptibility will be used to develop classifiers and neural networks that allow for seizure prediction on an individual and a group level first for GTCS only and then extend it to all seizure types.The project is highly feasible due an existing working relationship and availability of data. The host institute has a unique database of approximately 300 ANS recordings of children with diagnosed epilepsy. The expected outcome are biomarkers that are relevant across patients and allow for a reliable individualized seizure prediction in most patients. The results are expected to help improving the quality of life in PWE, their relatives and caregivers, and possibly improve controllability of seizures and reduce health care costs.

癫痫是最常见的神经系统疾病之一，对患者有深远的影响。患者将癫痫发作何时发生的不确定性描述为他们日常生活中最大的负担。这种疾病本身会导致反复发作的癫痫发作，这源于神经元群的过度放电，并已被证明会改变自主神经系统（ANS）的活动。 多模式腕带提供了一种非侵入性和易于使用的可能性，以连续监测ANS功能。然而，生物标志物和癫痫发作预测算法仍然缺乏特异性，这是由于ANS活动中存在的高个体间和个体内变异性。我的项目的总体目标是确定癫痫发作易感性的生物标志物，并开发癫痫发作预测算法。我假设，在ANS的子系统内和跨子系统显示自己的中枢活动，遵循多模态模式，由电刺激引起的改变。与发作前阶段大脑的过度兴奋性相关，这可能会改变ANS子系统的中央驱动，例如心脏，呼吸和皮肤电系统，我预计表达子系统之间共享的信息量的多模态标记物会增加。为了分析多模态ANS变化，我将分析连续心率，呼吸率，记录癫痫发作时在手腕处测量的皮肤电活动和外周体温，并基于连续视频EEG记录进行分类。为了测试理论启发的生物标志物，我将通过使用子系统内的熵测度来表征单峰信号的信息内容，并通过使用互信息测度来表征信息交换。开发一种癫痫发作预测算法，可以在ANS数据中识别癫痫发作的高风险。通过使用单模态和多模态的生物标志物，表明癫痫发作的易感性将被用来开发分类器和神经网络，允许癫痫发作预测的个人和组水平上的第一个GTCS，然后将其扩展到所有的癫痫发作类型。该项目是高度可行的，由于现有的工作关系和数据的可用性。主办机构拥有一个独特的数据库，其中包含约300个诊断为癫痫的儿童的ANS记录。预期结果是与患者相关的生物标志物，并允许在大多数患者中进行可靠的个体化癫痫发作预测。预计这些结果将有助于改善PWE及其亲属和照顾者的生活质量，并可能提高癫痫发作的可控性并降低医疗保健成本。