Distinction of an Assortment of Deep Brain Stimulation Parameter Configurations for Treating Parkinson’s Disease Using Machine Learning with Quantification of Tremor Response through a Conformal Wearable and Wireless Inertial Sensor
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.
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