Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

Abstract

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.

Quantifying unsupported sitting posture impairments in humans with cervical spinal cord injury using a head-mounted IMU sensor

“…For each EO and EC testing, x (anterior-posterior [AP]), y (vertical), and z (medial-lateral [ML]) acceleration time series signals were recorded. Cumulative sway motion (CSM) was calculated using the following approximations [18]:…”

“…where MB is the average mass of the brain (MB = 1.2 kg), a 2 is calculated as (ax 2 + ay 2 + az 2 ), and t is the period of time. CSM represents the global sway motion and evaluates postural stability during unsupported sitting [18]. For the spectral analysis, the timeseries signals were calculated using a discrete Fourier Transform [19] to generate a power spectral density (PSD).…”