Abstract
Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.
Highlights
Deep brain stimulation offers a significant advance for the treatment of people with Parkinson’s disease
The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning
The attenuating trend of Parkinson’s disease hand tremor is quantified through the BioStamp nPoint, which constitutes a conformal wearable and wireless inertial sensor system, and the three dimensional orthogonal accelerometer signal is post-processed to the respective acceleration magnitude using Python
Summary
Deep brain stimulation offers a significant advance for the treatment of people with Parkinson’s disease. The essence of efficacious intervention through the deep brain stimulation system is contingent upon converging the parameter configuration to an optimized setting, which can present a laborious process [2]-[7]. The deep brain stimulation system parameter optimization process relies upon the establishment of quantified feedback, such as through ordinal scales. The medication therapy diminishes in efficacy, and an alternative intervention is sought, such as the thalamotomy and pallidotomy. These neurological techniques permanently disrupt pathways pertaining to the thalamus and globus pallidus internal segment [20] [34] [39] [40] [41] [42]
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