Detection of Traumatic Brain Injury Using a Standard Machine Learning Pipeline in Mouse and Human Sleep Electroencephalogram
Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. TBI is defined as an alteration in brain functioning or brain pathology initiated by external impacts, such as blunt trauma, penetrating objects, or blast waves which can cause a wide range of functional short- or long-term changes affecting thinking, sensation, language, and emotion, and perhaps most prominently, sleep. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent post-concussive symptoms are most likely multifactorial and the underlying mechanism is not well understood. Currently, there are no prognostic markers to predict individuals who are most at risk. Thus, novel approaches to the precise detection and prognostication of mTBI is of utmost importance. The sleep electroencephalogram (EEG) provides a direct window into neuronal activity during an otherwise highly stereotyped behavioral state and represents a promising quantitative measure for TBI diagnosis and prognosis. With the ever-evolving domain of machine learning, deep convolutional neural networks, and the development of better architectures, these approaches hold promise to solve some of the long-entrenched challenges of personalized medicine for uses in recommendation systems and/or in health monitoring systems. In particular, advanced EEG analysis to identify putative EEG biomarkers of neurological disease could be highly relevant in the prognostication of mild TBI, an otherwise heterogeneous disorder with a wide range of affected phenotypes and disability levels. In this work, we investigate the use of various machine learning techniques on a cohort of mice and human subjects with sleep EEG recordings from overnight, in-lab, diagnostic polysomnography (PSG) from human subjects and 24 hours recording from mice subjects. An optimal scheme is explored for the classification of TBI versus non-TBI control subjects. The results are promising with an accuracy of ∼95\% in mice and ∼75\% in humans. We are thus confident that, with additional data and further studies, we would be able to build a generalized model to detect TBI accurately, not only via attended, in-lab PSG recordings, but also in practical scenarios such as EEG data obtained from simple wearables in daily life.