TY - JOUR
T1 - A deep learning-based approach to diagnose mild traumatic brain injury using audio classification
AU - Wall, Conor
AU - Powell, Dylan
AU - Young, Fraser
AU - Zynda, Aaron J.
AU - Stuart, Samuel
AU - Covassin, Tracey
AU - Godfrey, Alan
N1 - 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.
PY - 2022/9/28
Y1 - 2022/9/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85139242597&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0274395
DO - 10.1371/journal.pone.0274395
M3 - Article
C2 - 36170287
AN - SCOPUS:85139242597
SN - 1932-6203
VL - 17
JO - PLoS One
JF - PLoS One
IS - 9
M1 - e0274395
ER -