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Entropy 2019, 21(4), 346; https://doi.org/10.3390/e21040346

Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson’s Disease

1
Faculty of Engineering, National University of Entre Ríos: route 11, Km 10, 3100 Oro Verde, Argentina
2
Faculty of Life and Health Sciences, Autonomous University of Entre Ríos, Alem 217, 3100 Paraná, Argentina
3
Faculty of Science and Technology, Autonomous University of Entre Ríos, route 11, Km 10.5, 3100 Oro Verde, Argentina
4
GDDP, Department of Electronic Engineering, School of Engineering, Universitat de Valencia, Burjassot, 46100 Valencia, Spain
*
Authors to whom correspondence should be addressed.
Received: 28 February 2019 / Revised: 21 March 2019 / Accepted: 26 March 2019 / Published: 29 March 2019
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Abstract

Deep Brain Stimulation (DBS) of the Subthalamic Nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson’s Disease (PD) who do not adequately respond to pharmacological treatment, or have related side effects. During surgery for the implantation of a DBS system, signals are obtained through microelectrodes recordings (MER) at different depths of the brain. These signals are analyzed by neurophysiologists to detect the entry and exit of the STN region, as well as the optimal depth for electrode implantation. In the present work, a classification model is developed and supervised by the K-nearest neighbour algorithm (KNN), which is automatically trained from the 18 temporal features of MER registers of 14 patients with PD in order to provide a clinical support tool during DBS surgery. We investigate the effect of different standardizations of the generated database, the optimal definition of KNN configuration parameters, and the selection of features that maximize KNN performance. The results indicated that KNN trained with data that was standardized per cerebral hemisphere and per patient presented the best performance, achieving an accuracy of 94.35% (p < 0.001). By using feature selection algorithms, it was possible to achieve 93.5% in accuracy in selecting a subset of six features, improving computation time while processing in real time. View Full-Text
Keywords: deep brain stimulation-DBS; microelectrode registers-MER; K-nearest neighbour-KNN algorithm; feature selection; Parkinson’s disease deep brain stimulation-DBS; microelectrode registers-MER; K-nearest neighbour-KNN algorithm; feature selection; Parkinson’s disease
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Bellino, G.M.; Schiaffino, L.; Battisti, M.; Guerrero, J.; Rosado-Muñoz, A. Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson’s Disease. Entropy 2019, 21, 346.

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