Volume No. :   9

Issue No. :  2

Year :  2018

ISSN Print :  0976-2973

ISSN Online :  2321-581X


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



Address:   Dr. Venkateshwarla Rama Raju1,2,3
1CMR College of Engineering and Technology (UGC Autonomous), Dept of Computer Science and Engineering, Kandlakoya, Medchal Rd, Hyderabad, India-501401
2Nizam`s Inst of Medical Sciences, Biomedical, Neurology and Neurosurgery, Hyderabad, India
3Visiting Faculty: Biomedical Engineering Dept, Osmania University College of Eng (Autonomous)
*Corresponding Author
DOI No: 10.5958/2321-581X.2018.00020.X

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.
KEYWORDS:
Microelectrode-recording (MER), Parkinson`s disease (PD), STN-DBS, Classification and Prediction, Random Forest
Cite:
Venkateshwarla Rama Raju. Improving UPDRS and Efficacy of DBS with Microelectrode Recording of Subthalamic-Nuclei Deep Brain Stimulation (STN-DBS)–Classification and Prediction. Research J. Engineering and Tech. 2018;9(2): 143-149.
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