School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.
Vector Institute, MaRS Discovery District, Ontario, Canada.
BMJ Open. 2019 Jul 17;9(7):e029621. doi: 10.1136/bmjopen-2019-029621.
Coma is a deep state of unconsciousness that can be caused by a variety of clinical conditions. Traditional tests for coma outcome prediction are based mainly on a set of clinical observations. Recently, certain event-related potentials (ERPs), which are transient electroencephalogram (EEG) responses to auditory, visual or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (ie, emergence). However, such tests require the skills of clinical neurophysiologists, who are not commonly available in many clinical settings. Additionally, none of the current standard clinical approaches have sufficient predictive accuracies to provide definitive prognoses.
The objective of this study is to develop improved machine learning procedures based on EEG/ERP for determining emergence from coma.
Data will be collected from 50 participants in coma. EEG/ERP data will be recorded for 24 consecutive hours at a maximum of five time points spanning 30 days from the date of recruitment to track participants' progression. The study employs paradigms designed to elicit brainstem potentials, middle-latency responses, N100, mismatch negativity, P300 and N400. In the case of patient emergence, data are recorded on that occasion to form an additional basis for comparison. A relevant data set will be developed from the testing of 20 healthy controls, each spanning a 15-hour recording period in order to formulate a baseline. Collected data will be used to develop an automated procedure for analysis and detection of various ERP components that are salient to prognosis. Salient features extracted from the ERP and resting-state EEG will be identified and combined to give an accurate indicator of prognosis.
This study is approved by the Hamilton Integrated Research Ethics Board (project number 4840). Results will be disseminated through peer-reviewed journal articles and presentations at scientific conferences.
NCT03826407.
昏迷是一种深度无意识状态，可由多种临床情况引起。传统的昏迷结局预测测试主要基于一系列临床观察。最近，某些事件相关电位（ERPs），即听觉、视觉或触觉刺激的短暂脑电图（EEG）反应，已被引入作为阳性昏迷结局（即觉醒）的有用预测指标。然而，此类测试需要临床神经生理学家的技能，而在许多临床环境中，这些专家并不常见。此外，目前的任何标准临床方法都没有足够的预测准确性来提供明确的预后。
本研究的目的是开发基于 EEG/ERP 的改进机器学习程序，以确定昏迷患者是否能苏醒。
将从 50 名昏迷患者中收集数据。在招募日期后的 30 天内，最多在五个时间点上连续记录 24 小时的 EEG/ERP 数据，以跟踪参与者的进展。该研究采用旨在引出脑干电位、中潜伏期反应、N100、失匹配负波、P300 和 N400 的范式。在患者觉醒的情况下，将在该时记录数据，以形成额外的比较基础。通过对 20 名健康对照者进行测试，开发出一个相关的数据集，每个对照者的记录时间跨度为 15 小时，以制定基线。收集的数据将用于开发一种自动分析和检测对预后有重要意义的各种 ERP 成分的程序。从 ERP 和静息状态 EEG 中提取的显著特征将被识别并组合，以给出预后的准确指标。
这项研究得到了汉密尔顿综合研究伦理委员会的批准（项目编号 4840）。研究结果将通过同行评议的期刊文章和科学会议上的演讲进行传播。
NCT03826407。
