Peterson W, Ramakrishnan N, Browder K, Sanossian N, Nguyen P, Fink E. Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.
J Stroke Cerebrovasc Dis 2024;
33:107714. [PMID:
38636829 DOI:
10.1016/j.jstrokecerebrovasdis.2024.107714]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/15/2024] [Accepted: 04/06/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVES
We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed.
MATERIALS AND METHODS
Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke).
RESULTS
Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset.
CONCLUSIONS
Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95).
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