Book chapter
Role of Machine Learning for Classification of Movement Disorder and Deep Brain Stimulation Status
Wearable and Wireless Systems for Healthcare II, pp 99-112
24 Aug 2024
Abstract
Recently, machine learning has augmented the capability of the amalgamation of wearable and wireless systems for deep brain stimulation systems. Machine learning platforms have been applied to attain considerable classification accuracy for distinguishing between deep brain stimulation set to “On” and “Off” modes for Essential tremor and Parkinson’s disease. Other movement disorders, such as hemiplegic affected and unaffected limb pairs, have been successfully differentiated through machine learning classification. Central to these machine learning endeavors has been the application of wearable and wireless systems using inertial sensors, such as the accelerometer and gyroscope, to consolidate signal data into feature sets for machine learning classification. An assortment of prevalent machine learning platforms is discussed, such as J48 decision tree, K-nearest neighbor, logistic regression, support vector machine, multilayer perceptron neural network, and random forest. Machine learning is envisioned to serve an instrumental role for the objective of achieving closed-loop optimization of deep brain stimulation parameter configurations. In essence, machine learning is envisioned to function as a predominant role for the post-processing perspective of Network Centric Therapy.
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Details
- Title
- Role of Machine Learning for Classification of Movement Disorder and Deep Brain Stimulation Status
- Creators
- Robert LeMoyneTimothy MastroianniDonald WhitingNestor Tomycz
- Publication Details
- Wearable and Wireless Systems for Healthcare II, pp 99-112
- Series
- Smart Sensors, Measurement and Instrumentation
- Publisher
- Springer Nature Singapore; Singapore
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- SOM Dean - Research Administration
- Other Identifier
- 991021899209604721