Assessment of Machine Learning Classification Strategies for the Differentiation of Deep Brain Stimulation “On” and “Off” Status for Parkinson’s Disease Using a Smartphone as a Wearable and Wireless Inertial Sensor for Quantified Feedback

Abstract

The considerable advantage of integrating wearable and wireless systems with machine learning for the assessment of deep brain stimulation parameter configuration status is addressed. A wearable and wireless system, such as a smartphone with its accelerometer and gyroscope, provides the quantified basis for the efficacy determination of a treatment strategy. In particular, deep brain stimulation is well suited for being amalgamated with wearable and wireless systems. For a subject with Parkinson’s disease, deep brain stimulation set to “On” and “Off” status is distinguished through an assortment of machine learning algorithms, such as J48 decision tree, K-nearest neighbors, logistic regression, support vector machine, multilayer perceptron neural network, and random forest. The feature set is consolidated from the accelerometer signal and gyroscope signal from a smartphone using software automation. The appropriateness for these machine learning algorithms was assessed in terms of both classification accuracy and computational efficiency. These capabilities further refine the opportunities of machine learning classification being allocated local to the wearable and wireless system with an available Cloud computing resource. These findings establish a preliminary perspective regarding the utility of Network Centric Therapy, for which effectively real-time optimization of parameter configurations for deep brain stimulation can be developed in a patient-specific context. Furthermore, the real-time optimization process can be adaptive to the inherent temporal fluctuations of a progressive neurodegenerative movement disorder, such as Parkinson’s disease and Essential tremor.