Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

ABSTRACT:

Objectives

Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (TBI) history from those with stroke history and/or normal EEG.

Methods

Support vector machine (SVM) and K-nearest neighbors (KNN) models were generated with a diverse feature set from Temple EEG Corpus for both two-class classification of patients with TBI history from normal subjects and three-class classification of TBI, stroke and normal subjects.

Results

For two-class classification, an accuracy of 0.94 was achieved in 10-

SUBMITTER: Vivaldi N 

PROVIDER: S-EPMC9513823 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature