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AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction.

Publication ,  Journal Article
Zhan, X; Liu, Y; Cecchi, NJ; Callan, AA; Le Flao, E; Gevaert, O; Zeineh, MM; Grant, GA; Camarillo, DB
Published in: IEEE Trans Biomed Eng
September 2024

OBJECTIVE: Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human bodies. This study aimed to improve the head impact kinematics measurements obtained from instrumented mouthguards using deep learning to enhance traumatic brain injury (TBI) risk monitoring. METHODS: We developed one-dimensional convolutional neural network (1D-CNN) models to denoise mouthguard kinematics measurements for tri-axial linear acceleration and tri-axial angular velocity from 163 laboratory dummy head impacts. The performance of the denoising models was evaluated on three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Additionally, we performed a blind test on an on-field dataset of 118 college football impacts and a test on 413 post-mortem human subject (PMHS) impacts. RESULTS: On the dummy head impacts, the denoised kinematics showed better correlation with reference kinematics, with relative reductions of 36% for pointwise root mean squared error and 56% for peak absolute error. Absolute errors in six brain injury criteria were reduced by a mean of 82%. For maximum principal strain and maximum principal strain rate, the mean error reduction was 35% and 69%, respectively. On the PMHS impacts, similar denoising effects were observed and the peak kinematics after denoising were more accurate (relative error reduction for 10% noisiest impacts was 75.6%). CONCLUSION: The 1D-CNN denoising models effectively reduced errors in mouthguard-derived kinematics measurements on dummy and PMHS impacts. SIGNIFICANCE: This study provides a novel approach for denoising head kinematics measurements in dummy and PMHS impacts, which can be further validated on more real-human kinematics data before real-world applications.

Duke Scholars

Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

September 2024

Volume

71

Issue

9

Start / End Page

2759 / 2770

Location

United States

Related Subject Headings

  • Wearable Electronic Devices
  • Neural Networks, Computer
  • Mouth Protectors
  • Male
  • Humans
  • Head
  • Football
  • Deep Learning
  • Brain Injuries, Traumatic
  • Biomedical Engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhan, X., Liu, Y., Cecchi, N. J., Callan, A. A., Le Flao, E., Gevaert, O., … Camarillo, D. B. (2024). AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction. IEEE Trans Biomed Eng, 71(9), 2759–2770. https://doi.org/10.1109/TBME.2024.3392537
Zhan, Xianghao, Yuzhe Liu, Nicholas J. Cecchi, Ashlyn A. Callan, Enora Le Flao, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, and David B. Camarillo. “AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction.IEEE Trans Biomed Eng 71, no. 9 (September 2024): 2759–70. https://doi.org/10.1109/TBME.2024.3392537.
Zhan X, Liu Y, Cecchi NJ, Callan AA, Le Flao E, Gevaert O, et al. AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction. IEEE Trans Biomed Eng. 2024 Sep;71(9):2759–70.
Zhan, Xianghao, et al. “AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction.IEEE Trans Biomed Eng, vol. 71, no. 9, Sept. 2024, pp. 2759–70. Pubmed, doi:10.1109/TBME.2024.3392537.
Zhan X, Liu Y, Cecchi NJ, Callan AA, Le Flao E, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction. IEEE Trans Biomed Eng. 2024 Sep;71(9):2759–2770.

Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

September 2024

Volume

71

Issue

9

Start / End Page

2759 / 2770

Location

United States

Related Subject Headings

  • Wearable Electronic Devices
  • Neural Networks, Computer
  • Mouth Protectors
  • Male
  • Humans
  • Head
  • Football
  • Deep Learning
  • Brain Injuries, Traumatic
  • Biomedical Engineering