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

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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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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.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.rights.uri info:eu-repo/semantics/openAccess
dc.source 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.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 info:eu-repo/semantics/article
dc.date.updated 2020-09-24T14:20:13Z
dc.identifier.doi https://doi.org/10.3390/e21040346
dc.identifier.idgrec 140461

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