Lauren Shreve1, Arshdeep Kaur1, Christopher Vo1, Jennifer Wu2, Jessica M Cassidy1, Andrew Nguyen1, Robert J Zhou1, Thuong B Tran1, Derek Z Yang1, Ariana I Medizade1, Bharath Chakravarthy3, Wirachin Hoonpongsimanont3, Erik Barton3, Wengui Yu1, Ramesh Srinivasan4, Steven C Cramer5. 1. Department of Neurology, University of California, Irvine, Irvine, California. 2. Department of Neurology, University of California, Irvine, Irvine, California; Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, California. 3. Department of Emergency Medicine, University of California, Irvine, Irvine, California. 4. Department of Cognitive Sciences, University of California, Irvine, Irvine, California; Department of Biomedical Engineering, University of California, Irvine, Irvine, California. 5. Department of Neurology, University of California, Irvine, Irvine, California; Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, California. Electronic address: scramer@uci.edu.
Abstract
BACKGROUND: Early diagnosis of stroke optimizes reperfusion therapies, but behavioral measures have incomplete accuracy. Electroencephalogram (EEG) has high sensitivity for immediately detecting brain ischemia. This pilot study aimed to evaluate feasibility and utility of EEG for identifying patients with a large acute ischemic stroke during Emergency Department (ED) evaluation, as these data might be useful in the prehospital setting. METHODS: A 3-minute resting EEG was recorded using a dense-array (256-lead) system in patients with suspected acute stroke arriving at the ED of a US Comprehensive Stroke Center. RESULTS: An EEG was recorded in 24 subjects, 14 with acute cerebral ischemia (including 5 with large acute ischemic stroke) and 10 without acute cerebral ischemia. Median time from stroke onset to EEG was 6.6 hours; and from ED arrival to EEG, 1.9 hours. Delta band power (P = .004) and the alpha/delta frequency band ratio (P = .0006) each significantly distinguished patients with large acute ischemic stroke (n = 5) from all other patients with suspected stroke (n = 19), with the best diagnostic utility coming from contralesional hemisphere signals. Larger infarct volume correlated with higher EEG power in the alpha/delta frequency band ratio within both the ipsilesional (r = -0.64, P = .013) and the contralesional (r = -0.78, P = .001) hemispheres. CONCLUSIONS: Within hours of stroke onset, EEG measures (1) identify patients with large acute ischemic stroke and (2) correlate with infarct volume. These results suggest that EEG measures of brain function may be useful to improve diagnosis of large acute ischemic stroke in the ED, findings that might be useful to pre-hospital applications.
BACKGROUND: Early diagnosis of stroke optimizes reperfusion therapies, but behavioral measures have incomplete accuracy. Electroencephalogram (EEG) has high sensitivity for immediately detecting brain ischemia. This pilot study aimed to evaluate feasibility and utility of EEG for identifying patients with a large acute ischemic stroke during Emergency Department (ED) evaluation, as these data might be useful in the prehospital setting. METHODS: A 3-minute resting EEG was recorded using a dense-array (256-lead) system in patients with suspected acute stroke arriving at the ED of a US Comprehensive Stroke Center. RESULTS: An EEG was recorded in 24 subjects, 14 with acute cerebral ischemia (including 5 with large acute ischemic stroke) and 10 without acute cerebral ischemia. Median time from stroke onset to EEG was 6.6 hours; and from ED arrival to EEG, 1.9 hours. Delta band power (P = .004) and the alpha/delta frequency band ratio (P = .0006) each significantly distinguished patients with large acute ischemic stroke (n = 5) from all other patients with suspected stroke (n = 19), with the best diagnostic utility coming from contralesional hemisphere signals. Larger infarct volume correlated with higher EEG power in the alpha/delta frequency band ratio within both the ipsilesional (r = -0.64, P = .013) and the contralesional (r = -0.78, P = .001) hemispheres. CONCLUSIONS: Within hours of stroke onset, EEG measures (1) identify patients with large acute ischemic stroke and (2) correlate with infarct volume. These results suggest that EEG measures of brain function may be useful to improve diagnosis of large acute ischemic stroke in the ED, findings that might be useful to pre-hospital applications.
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