Portable mTBI Assessment Using Temporal and Frequency Analysis of Speech.

This paper shows that extraction and analysis of various acoustic features from speech using mobile devices can allow the detection of patterns that could be indicative of neurological trauma. This may pave the way for new types of biomarkers and diagnostic tools. Toward this end, we created a mobile application designed to diagnose mild traumatic brain injuries (mTBI) such as concussions. Using this application, data were collected from youth athletes from 47 high schools and colleges in the Midwestern United States. In this paper, we focus on the design of a methodology to collect speech data, the extraction of various temporal and frequency metrics from that data, and the statistical analysis of these metrics to find patterns that are indicative of a concussion. Our results suggest a strong correlation between certain temporal and frequency features and the likelihood of a concussion.