A Novel Method to Assist Clinical Management of Mild Traumatic Brain Injury by Classifying Patient Subgroups Using Wearable Sensors and Exertion Testing: A Pilot Study

Abstract

Although injury mechanisms of mild traumatic brain injury (mTBI) may be similar across patients, it is becoming increasingly clear that patients cannot be treated as one homogenous group. Several predominant symptom clusters (PSC) have been identified, each requiring specific and individualised treatment plans. However, objective methods to support these clinical decisions are lacking. This pilot study explored whether wearable sensor data collected during the Buffalo Concussion Treadmill Test (BCTT) combined with a deep learning approach could accurately classify mTBI patients with physiological PSC versus vestibulo-ocular PSC. A cross-sectional design evaluated a convolutional neural network model trained with electrocardiography (ECG) and accelerometry data. With a leave-one-out approach, this model classified 11 of 12 (92%) patients with physiological PSC and 3 of 5 (60%) patients with vestibulo-ocular PSC. The same classification accuracy was observed in a model only using accelerometry data. Our pilot results suggest that adding wearable sensors during clinical tests like the BCTT, combined with deep learning models, may have the utility to assist management decisions for mTBI patients in the future. We reiterate that more validation is needed to replicate the current results.

Keywords

• mTBI
• convolutional neural network
• ecological validity
• neurophysiology
• biomechanics
• symptom clusters