Li Xiaojin, Huang Yan, Tao Shiqiang, Cui Licong, Lhatoo Samden D, Zhang Guo-Qiang
University of Texas Health Science Center at Houston, Houston, TX 77030.
Institute for Biomedical Informatics, University of Kentucky, Lexington, KY 40506.
AMIA Annu Symp Proc. 2020 Mar 4;2019:1111-1120. eCollection 2019.
Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the data for developing computational methods) seizure signal datasets with associated tools for seizure data management and visualization. We introduce SeizureBank, a web-based data management and visualization system for epileptic seizures. SeizureBank comes with a built-in seizure data preparation pipeline and web-based interfaces for querying, exporting and visualizing seizure-related signal data. In this pilot study, 224 seizures from 115 patients were extracted from over one terabyte of signal data and deposited in SeizureBank. To demonstrate the value of this approach, we develop a feature-based seizure identification approach and evaluate the performance on a variety of data sources. The results can serve as a cross-dataset evaluation benchmark for future seizure identification studies.
全球约有6000万人患有癫痫发作。癫痫研究的机器学习方法面临的一个关键挑战是缺乏可供分析的(在使用数据开发计算方法时无需额外预处理)癫痫发作信号数据集的数据资源以及癫痫发作数据管理和可视化的相关工具。我们推出了SeizureBank,这是一个基于网络的癫痫发作数据管理和可视化系统。SeizureBank附带了一个内置的癫痫发作数据准备管道以及用于查询、导出和可视化癫痫发作相关信号数据的网络界面。在这项初步研究中,从超过1太字节的信号数据中提取了115名患者的224次癫痫发作,并存入了SeizureBank。为了证明这种方法的价值,我们开发了一种基于特征的癫痫发作识别方法,并在各种数据源上评估其性能。这些结果可作为未来癫痫发作识别研究的跨数据集评估基准。