发作性
癫痫外科
癫痫
计算机科学
脑电图
多样性(控制论)
数据科学
人工智能
神经科学
心理学
作者
John M. Bernabei,Li A,Alexander B. Silva,Robert F. Smith,Kristin M. Gunnarsdottir,Ian Ong,Kathryn A. Davis,Nishant Sinha,Sridevi V. Sarma,Brian Litt
出处
期刊:Brain
[Oxford University Press]
日期:2023-01-10
卷期号:146 (6): 2248-2258
被引量:15
标识
DOI:10.1093/brain/awad007
摘要
Abstract Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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