Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
J Neural Eng. 2023 Aug 10;20(4). doi: 10.1088/1741-2552/aceca1.
Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.
癫痫是一种以反复发作为特征的神经系统疾病，发作的严重程度差异很大，从临床无症状到长时间抽搐不等。衡量严重程度对于指导治疗至关重要，特别是在无法完全控制的情况下。目前用于指导治疗的标准是发作日记，但它对事件的持续时间或发作活动在大脑中的传播不敏感。我们提出了一种定量的发作严重程度评分，该评分结合了脑电图（EEG）和临床数据，并展示了如何利用它来指导癫痫治疗。我们收集了 54 名癫痫患者的颅内 EEG 和临床症状数据，这些患者在住院期间进行了侵入性术前评估，共发生了 256 次发作。我们应用绝对斜率算法对 EEG 记录进行分析，以识别发作通道。从这些数据中，我们开发了一种使用非负矩阵分解来结合发作持续时间、传播和症状学的发作严重程度评分。为了验证，我们评估了它与独立的癫痫负担测量指标（发作类型、癫痫持续时间、药物负荷的药代动力学模型和对癫痫手术的反应）的相关性。我们还研究了发作严重程度评分与发作前网络特征之间的关系。发作严重程度评分通过客观地从可用电极的记录中描绘发作持续时间和传播，增强了临床分类。较低的发作前药物负荷与较高的发作严重程度评分相关（=0.018，97.5%置信区间= [-1.242，-0.116]），术前严重程度较低与手术结果较好相关（=0.042）。在 85%的具有多种发作类型的患者中，与基线相比，更大的发作前变化与更高的严重程度相关。我们提出了一种包括 EEG 和临床特征的定量发作严重程度测量方法，该方法在金标准住院记录上得到了验证。我们提供了一个扩展我们工具的效用的框架，将其扩展到可移动 EEG 设备，并将其与可穿戴传感器测量的发作症状学联系起来，并将其作为推进数据驱动的癫痫护理的工具。
