Citation

Daudet L, Yadav N, Perez M, Poellabauer C, Schneider S, Huebner A. IEEE J. Biomed. Health Inform. 2016; 21(2): 496-506.

Copyright

(Copyright © 2016, Institute of Electrical and Electronics Engineers)

DOI

10.1109/JBHI.2016.2633509

PMID

27913365

Abstract

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 was collected from youth athletes from 47 high schools and colleges in the 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 nd 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.

Language: en