Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

PMID: 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.

Authors

William Peterson

University of Virginia, Charlottesville, VA, United States. Electronic address: wcp7cp@virginia.edu.
Nithya Ramakrishnan

Baylor College of Medicine, Houston, TX, United States.
Krag Browder

Aspen Insights, Dallas, TX, United States.
Nerses Sanossian

Roxanna Todd Hodges Stroke Program, United States; Keck School of Medicine of the University of Southern California, United States.
Peggy Nguyen

Keck School of Medicine of the University of Southern California, United States.
Ezekiel Fink

Houston Hospital, Houston, TX, United States; Weill Cornell School of Medicine Sciences, New York, NY, United States.

Keywords

Adult Aged Aged, 80 and over Brain Brain Waves Case-Control Studies Databases, Factual Diagnosis, Computer-Assisted Diagnosis, Differential Electroencephalography Female Humans Ischemic Stroke Machine Learning Male Middle Aged Predictive Value of Tests Reproducibility of Results Retrospective Studies Signal Processing, Computer-Assisted Time Factors

External Resources

View on PubMed Access via DOI PubMed (38636829)

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

Abstract

Authors

Keywords

External Resources

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