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

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 classif...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2021-11, Vol.68 (11), p.3205-3216
Main Authors: Vivaldi, Nicolas, Caiola, Michael, Solarana, Krystyna, Ye, Meijun
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Brain
Brain injuries
Brain modeling
Classification
Discriminant analysis
EEG
EEG database
Electroencephalography
Head injuries
History
Indexing
Injury analysis
Learning algorithms
Machine learning
machine learning (ML)
Matlab
Performance evaluation
Stroke
Stroke (medical condition)
Support vector machines
Traumatic brain injury
traumatic brain injury (TBI)
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Summary: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-fold cross validation (CV), and 0.76 in independent validation (IV). For three-class classification, 0.85 and 0.71 accuracy were reached in CV and IV respectively. Overall, linear discriminant analysis (LDA) feature selection and SVM models consistently performed well in both CV and IV and for both two-class and three-class classification. Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. But stroke patients showed a greater degree of change and had additional global decrease in theta power. Conclusions: Our study suggests that EEG data-driven machine learning can be a useful tool for TBI classification. Significance: Our study provides preliminary evidence that EEG ML algorithm can potentially provide specificity to separate different neurological conditions.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2021.3062502