The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia; Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, VIC, Australia.
The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia.
Clin Neurophysiol. 2019 Mar;130(3):368-378. doi: 10.1016/j.clinph.2018.11.024. Epub 2018 Dec 17.
The process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy.
We implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other 'similar' EEG events. We compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only.
In contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy datasets, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases automatic spike detection revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe.
Automatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy.
Our study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists.
在癫痫研究中，手动标记癫痫棘波以进行同时的脑电图 (EEG) 和静息状态功能磁共振成像 (rsfMRI) 分析是一项繁琐且主观的任务，需要人类专家完成。本研究旨在评估自动 EEG 棘波检测是否可以促进 EEG-rsfMRI 分析，并评估其作为癫痫临床工具的潜力。
我们为 19 例难治性局灶性癫痫数据集（来自 16 名患者）的 1 小时头皮 EEG 记录实现了一种快速算法，用于检测均匀的发作间癫痫样放电 (IED)。我们的方法基于匹配滤波的 IED 模板（源自人工标记），用于自动检测其他“相似”的 EEG 事件。我们比较了自动 IED 检测与仅使用人工 EEG 标记的标准分析的同时 EEG-rsfMRI 结果。
与人工标记相比，自动 IED 检测可以大大缩短检测 IED 并导出包含尖峰时间的输出文本文件的时间。在 13/19 例局灶性癫痫数据集中，基于自动尖峰检测方法的统计 EEG-rsfMRI 图与人工标记相当，在 6/19 例局灶性癫痫病例中，自动尖峰检测揭示了人类 EEG 标记未发现的额外脑区。我们的自动方法检测到的额外事件独立地揭示了与人工标记相似的激活模式。总体而言，与人工标记相比，自动 IED 检测在短时间内为 EEG-rsfMRI 分析提供了更大的统计功效。
自动尖峰检测是一种简单快速的方法，与 EEG-rsfMRI 分析中常见的手动 EEG 标记相比，它可以重现可比的结果，在某些情况下甚至可以提供更好的结果。
我们的研究表明，IED 检测算法可以有效地用于癫痫临床环境。这项工作进一步有助于将 EEG-rsfMRI 研究转化为快速、可靠且易于使用的癫痫学家临床工具。
