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Article

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

1
AltaML Inc., Edmonton, AB T5J 3N9, Canada
2
PROTXX Inc., Menlo Park, CA 94025, USA
3
PROTXX Medical Ltd., 120, 4838 Richard Road SW, Calgary, AB T3E 6L1, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Munoz-Organero
Sensors 2021, 21(21), 7417; https://doi.org/10.3390/s21217417
Received: 3 September 2021 / Revised: 3 November 2021 / Accepted: 3 November 2021 / Published: 8 November 2021
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)
Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. View Full-Text
Keywords: machine learning; wearable sensor; concussion; physiological impairment; vestibular; neurological machine learning; wearable sensor; concussion; physiological impairment; vestibular; neurological
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MDPI and ACS Style

Hope, A.J.; Vashisth, U.; Parker, M.J.; Ralston, A.B.; Roper, J.M.; Ralston, J.D. Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment. Sensors 2021, 21, 7417. https://doi.org/10.3390/s21217417

AMA Style

Hope AJ, Vashisth U, Parker MJ, Ralston AB, Roper JM, Ralston JD. Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment. Sensors. 2021; 21(21):7417. https://doi.org/10.3390/s21217417

Chicago/Turabian Style

Hope, Alex J., Utkarsh Vashisth, Matthew J. Parker, Andreas B. Ralston, Joshua M. Roper, and John D. Ralston. 2021. "Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment" Sensors 21, no. 21: 7417. https://doi.org/10.3390/s21217417

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