TY - JOUR
T1 - Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods
AU - Peterson, William
AU - Ramakrishnan, Nithya
AU - Browder, Krag
AU - Sanossian, Nerses
AU - Nguyen, Peggy
AU - Fink, Ezekiel
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/6
Y1 - 2024/6
N2 - 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).
AB - 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).
KW - Electroencephalogram (EEG)
KW - Feature engineering
KW - Ischemic stroke
KW - Large vessel occlusion
KW - Machine learning
KW - Prehospital stroke scale
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U2 - 10.1016/j.jstrokecerebrovasdis.2024.107714
DO - 10.1016/j.jstrokecerebrovasdis.2024.107714
M3 - Article
C2 - 38636829
AN - SCOPUS:85190883908
SN - 1052-3057
VL - 33
JO - Journal of Stroke and Cerebrovascular Diseases
JF - Journal of Stroke and Cerebrovascular Diseases
IS - 6
M1 - 107714
ER -