A deep learning-based approach to diagnose mild traumatic brain injury using audio classification

Wall, Conor, Powell, Dylan, Young, Fraser, Zynda, Aaron J., Stuart, Sam, Covassin, Tracey and Godfrey, Alan (2022) A deep learning-based approach to diagnose mild traumatic brain injury using audio classification. PLoS ONE, 17 (9). e0274395. ISSN 1932-6203

journal.pone.0274395.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview
Official URL: https://doi.org/10.1371/journal.pone.0274395


Mild traumatic brain injury (mTBI or concussion) is receiving increased attention due to the incidence in contact sports and limitations with subjective (pen and paper) diagnostic approaches. If an mTBI is undiagnosed and the athlete prematurely returns to play, it can result in serious short-term and/or long-term health complications. This demonstrates the importance of providing more reliable mTBI diagnostic tools to mitigate misdiagnosis. Accordingly, there is a need to develop reliable and efficient objective approaches with computationally robust diagnostic methods. Here in this pilot study, we propose the extraction of Mel Frequency Cepstral Coefficient (MFCC) features from audio recordings of speech that were collected from athletes engaging in rugby union who were diagnosed with an mTBI or not. These features were trained on our novel particle swarm optimised (PSO) bidirectional long short-term memory attention (Bi-LSTM-A) deep learning model. Little-to-no overfitting occurred during the training process, indicating strong reliability of the approach regarding the current test dataset classification results and future test data. Sensitivity and specificity to distinguish those with an mTBI were 94.7% and 86.2%, respectively, with an AUROC score of 0.904. This indicates a strong potential for the deep learning approach, with future improvements in classification results relying on more participant data and further innovations to the Bi-LSTM-A model to fully establish this approach as a pragmatic mTBI diagnostic tool.

Item Type: Article
Additional Information: Funding information: The work was supported by the Private Physiotherapy Education fund (349), Private Physiotherapy Education fund (3368) and the Faculty of Engineering and Environment at Northumbria University, UK.
Subjects: A100 Pre-clinical Medicine
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation
Depositing User: John Coen
Date Deposited: 29 Sep 2022 07:54
Last Modified: 29 Sep 2022 08:00
URI: https://nrl.northumbria.ac.uk/id/eprint/50248

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics