Mauro Caffarelli, Roxanne Simmons, Illya Tolokh, Vishnu Karukonda, Elan L Guterman, Wade Smith, Christine K Fox, M Brandon Westover, Edilberto Amorim
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COIN was independently calculated from consecutive 4-second EEG epochs. Student t-test and logistic regression were used to assess COIN performance in stroke size discrimination across the entire recording; random forest classification was used to determine COIN performance in limited EEG time windows ranging from 5 to 30 minutes in duration.</p><p><strong>Results: </strong>Thirty-five patients with mean age 67 (SD ± 17) years were analyzed with mean 4.5 ± 1.3 hours of clean EEG per patient. Ten patients had large stroke and 25 had small stroke. Participants with large strokes had larger COIN values than those with small strokes (-53 vs. -16, P = 0.0001). Logistic regression for stroke size classification model showed accuracy 83% ± 8%, sensitivity 70%±15%, specificity 88%±8%, and area under the receiver operator curve 0.75±0.10. Random Forest Classification performance was similar using 5 or 30 minutes of EEG data with accuracy 81% to 82%, specificity 91% to 92%, and sensitivity 55% to 58%, respectively.</p><p><strong>Conclusions: </strong>COIN differentiated large from small acute ischemic strokes in this single-center cohort. Prospective evaluation in larger multicenter data sets is necessary to determine COIN utility as an aid for bedside detection of large ischemic strokes in contexts where neuroimaging cannot be easily obtained or when neurologic examination is limited by sedation or neuromuscular blockade.</p>","PeriodicalId":15516,"journal":{"name":"Journal of Clinical Neurophysiology","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Quantitative Electroencephalographic Index for Stroke Detection in Adults.\",\"authors\":\"Mauro Caffarelli, Roxanne Simmons, Illya Tolokh, Vishnu Karukonda, Elan L Guterman, Wade Smith, Christine K Fox, M Brandon Westover, Edilberto Amorim\",\"doi\":\"10.1097/WNP.0000000000001151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Electroencephalography (EEG) remains underutilized for stroke characterization. We sought to assess the performance of the EEG Correlate Of Injury to the Nervous system (COIN) index, a quantitative metric designed for stroke recognition in children, in discriminating large from small ischemic strokes in adults.</p><p><strong>Methods: </strong>Retrospective, single-center cohort of adults with acute (within 7 days) ischemic stroke who underwent at least 8 hours of continuous EEG monitoring in hospital. Stroke size was categorized as large or small based on a threshold of 100 mL using the ABC/2 approach. EEG data were processed on MATLAB. COIN was independently calculated from consecutive 4-second EEG epochs. 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引用次数: 0
摘要
目的:脑电图(EEG)仍未充分利用中风的特征。我们试图评估脑电图神经系统损伤相关指数(COIN)的性能,这是一种用于儿童中风识别的定量指标,用于区分成人缺血性中风的大小。方法:回顾性、单中心队列研究急性(7天内)缺血性脑卒中成人患者,在医院接受至少8小时连续脑电图监测。使用ABC/2方法,以100 mL为阈值,将脑卒中大小分为大或小。利用MATLAB对脑电数据进行处理。从连续的4秒脑电epoch独立计算COIN。使用学生t检验和逻辑回归来评估在整个记录中对笔划大小的区分;随机森林分类用于确定在有限的EEG时间窗(5 ~ 30分钟)内的COIN性能。结果:35例患者平均年龄67 (SD±17)岁,平均4.5±1.3小时干净脑电图。10例为大卒中,25例为小卒中。大卒中患者的COIN值大于小卒中患者(-53 vs. -16, P = 0.0001)。脑卒中大小分类模型的Logistic回归分析准确率为83%±8%,灵敏度为70%±15%,特异性为88%±8%,受试者操作曲线下面积为0.75±0.10。随机森林分类在使用5分钟或30分钟脑电图数据时表现相似,准确率为81% ~ 82%,特异性为91% ~ 92%,灵敏度为55% ~ 58%。结论:在该单中心队列中,COIN可区分大急性缺血性卒中和小急性缺血性卒中。在神经成像不容易获得或神经检查受镇静或神经肌肉阻滞限制的情况下,有必要对更大的多中心数据集进行前瞻性评估,以确定COIN作为床边检测大面积缺血性中风的辅助工具的效用。
A Quantitative Electroencephalographic Index for Stroke Detection in Adults.
Purpose: Electroencephalography (EEG) remains underutilized for stroke characterization. We sought to assess the performance of the EEG Correlate Of Injury to the Nervous system (COIN) index, a quantitative metric designed for stroke recognition in children, in discriminating large from small ischemic strokes in adults.
Methods: Retrospective, single-center cohort of adults with acute (within 7 days) ischemic stroke who underwent at least 8 hours of continuous EEG monitoring in hospital. Stroke size was categorized as large or small based on a threshold of 100 mL using the ABC/2 approach. EEG data were processed on MATLAB. COIN was independently calculated from consecutive 4-second EEG epochs. Student t-test and logistic regression were used to assess COIN performance in stroke size discrimination across the entire recording; random forest classification was used to determine COIN performance in limited EEG time windows ranging from 5 to 30 minutes in duration.
Results: Thirty-five patients with mean age 67 (SD ± 17) years were analyzed with mean 4.5 ± 1.3 hours of clean EEG per patient. Ten patients had large stroke and 25 had small stroke. Participants with large strokes had larger COIN values than those with small strokes (-53 vs. -16, P = 0.0001). Logistic regression for stroke size classification model showed accuracy 83% ± 8%, sensitivity 70%±15%, specificity 88%±8%, and area under the receiver operator curve 0.75±0.10. Random Forest Classification performance was similar using 5 or 30 minutes of EEG data with accuracy 81% to 82%, specificity 91% to 92%, and sensitivity 55% to 58%, respectively.
Conclusions: COIN differentiated large from small acute ischemic strokes in this single-center cohort. Prospective evaluation in larger multicenter data sets is necessary to determine COIN utility as an aid for bedside detection of large ischemic strokes in contexts where neuroimaging cannot be easily obtained or when neurologic examination is limited by sedation or neuromuscular blockade.
期刊介绍:
The Journal of Clinical Neurophysiology features both topical reviews and original research in both central and peripheral neurophysiology, as related to patient evaluation and treatment.
Official Journal of the American Clinical Neurophysiology Society.