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Research Journal of Engineering and Technology
Year : 2018, Volume : 9, Issue : 2
First page : ( 143) Last page : ( 149)
Print ISSN : 0976-2973. Online ISSN : 2321-581X.
Article DOI : 10.5958/2321-581X.2018.00020.X

Improving UPDRS and Efficacy of DBS with Microelectrode Recording of Subthalamic-Nuclei Deep Brain Stimulation (STN-DBS)-Classification and Prediction

Dr. Raju Venkateshwarla Rama1,2

CMR College of Engineering and Technology (UGC Autonomous), Dept of Computer Science and Engineering, Kandlakoya, Medchal Rd, Hyderabad, India-501401

1Nizam's Inst of Medical Sciences, Biomedical, Neurology and Neurosurgery, Hyderabad, India

2Visiting Faculty: Biomedical Engineering Dept, Osmania University College of Eng (Autonomous)

Corresponding Author Email: drvrr@cmrcet.org, system@ou.ernet.in, idcoucea@hd1.vsnl.net.in

Online published on 20 December, 2018.

Abstract

In this study, we present classification and regression analysis to predict the UPDRS score and its enhancement after the microelectrode STN signal recording (MER) with DBS surgery (implantation of the microelectrode). We hypothesized that a data informed grouping of features extrapolated from MER signals of STN can envisage restore (by decreasing the tremor) and functioning the motor improvement in Parkinson's disease (PD) patients. A random—forest is used to account for unbalanced datasets and multiple observations per PD subject, and showed that only five features of STN-MER signals are sufficient and account for prognosting UPDRS advancement. This finding suggests that STN signal characteristics are maximum correlated to the extent of improvement motor restoration and motor behavior observed in STN DBS.

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Keywords

Microelectrode-recording (MER), Parkinson's disease (PD), STN-DBS, Classification and Prediction, Random Forest.

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