Evaluation of Machine Learning Algorithms for Classifying Deep Brain Stimulation Respective of ‘On’ and ‘Off’ Status

Abstract

Essential tremor is a prevalent neurodegenerative movement disorder. Deep brain stimulation represents a highly effective means of treatment, especially for scenarios for which traditional medical intervention is no longer feasible. One of the major post-operative challenges is the determination of an optimal set of tuning parameters. Optimizing the deep brain stimulation parameters can impart a time-intensive task to the clinician. The smartphone in the context of a wearable and wireless inertial sensor system offers the capability to objectively quantify the characteristics of the tremor. Machine learning in conjunction with a wearable and wireless inertial sensor system, such as a smartphone, can distinguish between disparate states, such as deep brain stimulation in ‘On’ and ‘Off’ status. Multiple machine learning classification techniques are available, such as the multilayer perceptron neural network, support vector machine, K-nearest neighbors, logistic regression, J-48 decision tree, and random forest. The objective of this research endeavor is to evaluate these six machine learning classification algorithms for classification of deep brain stimulation regarding ‘On’ and ‘Off’ status for Essential tremor during a reach and grasp task. The reach and grasp task is quantified through the smartphone as wearable and wireless inertial sensor system mounted to the dorsum of the hand and secured by latex glove. Multiple feature set scenarios are considered, such as recordings from both the accelerometer and gyroscope, accelerometer, and gyroscope. These scenarios facilitate the determination of the most robust machine learning algorithms. The multilayer perceptron neural network, support vector machine, K-nearest neighbors, and logistic regression achieve the highest classification accuracy for three feature set scenarios derived by recordings from both accelerometer and gyroscope, accelerometer, and gyroscope.