Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.
J Stroke Cerebrovasc Dis
; 33(6): 107714, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38636829
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 ANDMETHODS:
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).Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Valor Preditivo dos Testes
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Bases de Dados Factuais
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Eletroencefalografia
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Ondas Encefálicas
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Aprendizado de Máquina
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AVC Isquêmico
Limite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article