A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 12; pp. 3521 - 3530
• Main Authors: Islam, S., Shah, V., Gidde, S.T.R., Hutapea, P., Song, S. H., Picone, J., Kim, A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; brain deformation measurement; Brain modeling; Calibration; Deformation; Deformation effects; Head injuries; Learning algorithms; Machine learning; Magnetic field measurement; magnetic sensors; Magnetic tunneling; Multilayers; Neural networks; Position sensing; Sensor arrays; Sensors; Soft magnetic materials; Strain; Surgical implants; Traumatic brain injury; Viscoelasticity
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.2990071

A leading cause of traumatic brain injury (TBI) is intracranial brain deformation due to mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. In this paper, we describe a machine learning enabled wireless sensing system that predicts the trajectory of intracranial brain deformation. The sensing system consists of an implantable soft magnet and an external magnetic sensor array with a sensing volume of 12 × 12 × 4 mm 3 . Machine learning algorithm predicts the brain deformation by interpreting the magnetic sensor outputs created by the change in position of the implanted soft magnet. Three different machine learning models were trained on calibration data: (1) random forests, (2) k-nearest neighbors, and (3) a multi-layer perceptron-based neural network. These models were validated using both in vitro (a needle inserted into PVC gel) and in vivo (blast exposure to live and dead rat brains) experiments . The in vitro gel deformation predicted by these machine learning models showed excellent agreement with the camera measurements and had absolute error = 138 μm, Fréchet distance = 372 μm with normalized Procrustes disparity = 0.034. The in vivo brain deformation predicted by these models had absolute error = 50 μm, Fréchet distance = 95 μm with normalized Procrustes disparity = 0.055 for dead animal and absolute error = 125 μm, Fréchet distance = 289 μm with normalized Procrustes disparity = 0.2 for live animal respectively. These results suggest that the proposed machine learning enabled sensor system can be an effective tool for measuring in situ brain deformation.