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

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

dc.contributor.author	Martín Bellino, Gabriel
dc.contributor.author	Schiaffino, Luciano
dc.contributor.author	Battisti, Marisa
dc.contributor.author	Guerrero Martínez, Juan Francisco
dc.contributor.author	Rosado Muñoz, Alfredo
dc.date.accessioned	2020-09-24T14:20:12Z
dc.date.available	2020-09-24T14:20:12Z
dc.date.issued	2019
dc.identifier.citation	Martín Bellino, Gabriel Schiaffino, Luciano Battisti, Marisa Guerrero Martínez, Juan Francisco Rosado Muñoz, Alfredo 2019 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 21 346
dc.identifier.uri	https://hdl.handle.net/10550/75649
dc.description.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.
dc.language.iso	eng
dc.relation.ispartof	Entropy, 2019, vol. 21, num. 346
dc.subject	Enginyeria biomèdica
dc.subject	Neurologia
dc.title	Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson's Disease
dc.type	journal article	es_ES
dc.date.updated	2020-09-24T14:20:13Z
dc.identifier.doi	10.3390/e21040346
dc.identifier.idgrec	140461
dc.rights.accessRights	open access	es_ES