Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor
The concept of Network Centric Therapy represents an
amalgamation of wearable and wireless inertial sensor systems and machine
learning with access to a Cloud computing environment. The advent of Network
Centric Therapy is highly relevant to the treatment of Parkinson’s disease
through deep brain stimulation. Originally wearable and wireless systems for
quantifying Parkinson’s disease involved the use a smartphone to quantify hand
tremor. Although originally novel, the smartphone has notable issues as a
wearable application for quantifying movement disorder tremor. The smartphone
has evolved in a pathway that has made the smartphone progressively more
cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone
utilizes an inertial sensor package that is not certified for medical analysis,
and the trial data access a provisional Cloud computing environment through an
email account. These concerns are resolved with the recent development of a
conformal wearable and wireless inertial sensor system. This conformal wearable
and wireless system mounts to the hand with the profile of a bandage by
adhesive and accesses a secure Cloud computing environment through a segmented
wireless connectivity strategy involving a smartphone and tablet. Additionally,
the conformal wearable and wireless system is certified by the FDA of the United
States of America for ascertaining medical grade inertial sensor data. These
characteristics make the conformal wearable and wireless system uniquely suited
for the quantification of Parkinson’s disease treatment through deep brain
stimulation. Preliminary evaluation of the conformal wearable and wireless
system is demonstrated through the differentiation of deep brain stimulation
set to “On” and “Off” status. Based on the robustness of the acceleration
signal, this signal was selected to quantify hand tremor for the prescribed
deep brain stimulation settings. Machine learning classification using the
Waikato Environment for Knowledge Analysis (WEKA) was applied using the
multilayer perceptron neural network. The multilayer perceptron neural network
achieved considerable classification accuracy for distinguishing between the
deep brain stimulation system set to “On” and “Off” status through the
quantified acceleration signal data obtained by this recently developed
conformal wearable and wireless system.The research achievement
establishes a progressive pathway to the future objective of achieving deep
brain stimulation capabilities that promote closed-loop
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