Classification of Electroencephalogram in a Mouse Model of Traumatic Brain Injury Using Machine Learning Approaches

Abstract

Traumatic Brain Injury (TBI) is highly prevalent, affecting ~1% of the U.S. population, with lifetime economic costs estimated to be over $75 billion. In the U.S., there are about 50,000 deaths annually related to TBI, and many others are permanently disabled. However, it is currently unknown which individuals will develop persistent disability following TBI and what brain mechanisms underlie these distinct populations. The pathophysiologic causes for those are most likely multifactorial. Electroencephalogram (EEG) has been used as a promising quantitative measure for TBI diagnosis and prognosis. The recent rise of advanced data science approaches such as machine learning and deep learning holds promise to further analyze EEG data, looking for EEG biomarkers of neurological disease, including TBI. In this work, we investigated various machine learning approaches on our unique 24-hour recording dataset of a mouse TBI model, in order to look for an optimal scheme in classification of TBI and control subjects. The epoch lengths were 1 and 2 minutes. The results were promising with accuracy of ~80-90% when appropriate features and parameters were used using a small number of subjects (5 shams and 4 TBIs). We are thus confident that, with more data and studies, we would be able to detect TBI accurately, not only via long-term recordings but also in practical scenarios, with EEG data obtained from simple wearables in the daily life.

Fig. 1.

Experimental procedure for data acquisition.

Fig. 2.

Feature representation for training dataset (a) without applying decibel normalization (b) with decibel normalization.

Fig. 3.

The CNN architecture.

Fig. 4.

Cross-validation accuracy of various classifiers using 1 min epoch lengths of different sleep stages.

Fig. 5.

Cross-validation accuracy of various classifiers using 2 min epoch lengths of different sleep stages.