Citation

Vishwanath M, Jafarlou S, Shin I, Lim MM, Dutt N, Rahmani AM, Cao H. Sensors (Basel) 2020; 20(7): e2027.

Affiliation

Department of Biomedical Engineering, University of California, Irvine, CA 92607, USA.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s20072027

PMID

32260320

Abstract

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches.

RESULTS in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.

Language: en

Keywords

electroencephalogram (EEG); machine learning (ML); traumatic brain Injury (TBI)