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.
Highlights
Traumatic brain injury (TBI) is a common cause of disability and death in young people [1].Caused by external impact such as blunt trauma, penetrating objects or blast waves to the head, TBI is becoming increasingly prevalent with an estimated 1.6 million individuals sustaining mild traumatic brain injury each year
Lack of consensus regarding what constitutes mild traumatic brain injury (mTBI) adds to the complication of the under-diagnosis of the disease [3]
The recent advances in electroencephalogram (EEG) acquisition as well as quantitative electroencephalogram analysis have enabled a host of practical applications, such as detection of TBI in the field, making it more convenient compared to the counterpart of using bulky and resource-intensive CT scans
Summary
Traumatic brain injury (TBI) is a common cause of disability and death in young people [1]. Caused by external impact such as blunt trauma, penetrating objects or blast waves to the head, TBI is becoming increasingly prevalent with an estimated 1.6 million individuals sustaining mild traumatic brain injury (mTBI) each year. Major causes of TBI have been vehicle related collisions, sports or combat injuries causing brain damages, including tearing injuries of white matter or hematomas resulting in nausea, disturbed sleep patterns [2], dizziness, memory and/or concentration problems, emotional disturbances and seizures. The recent advances in electroencephalogram (EEG) acquisition as well as quantitative electroencephalogram (qEEG) analysis have enabled a host of practical applications, such as detection of TBI in the field, making it more convenient compared to the counterpart of using bulky and resource-intensive CT scans. The need for high-end lab equipment to acquire CT scans
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