Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

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dc.authorid0000-0001-8461-1404en_US
dc.authorid0000-0001-9610-9550en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorNour, Majid
dc.contributor.authorŞentürk, Ümit
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-09-12T06:05:09Z
dc.date.available2023-09-12T06:05:09Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionDeputyship for Research & Innovation, Ministry of Education in Saudi Arabiaen_US
dc.description.abstractIn this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.en_US
dc.description.sponsorshipDeputyship for Research & Innovation, Ministry of Education in Saudi Arabia [IFPIP: 1040-135-1443]en_US
dc.identifier.citationNour, M., Senturk, U., & Polat, K. (2023). Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN. Computers in Biology and Medicine, 161, 107031.en_US
dc.identifier.doi10.1016/j.compbiomed.2023.107031
dc.identifier.endpage15en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid37211002en_US
dc.identifier.scopus2-s2.0-85159607650en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.compbiomed.2023.107031
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11691
dc.identifier.volume161en_US
dc.identifier.wosWOS:001007130200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorŞentürk, Ümit
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParkinson's Disease (PD)en_US
dc.subjectEnsemble Learningen_US
dc.subjectDeep Learningen_US
dc.subject1D CNN Algoritmen_US
dc.subjectClassificationen_US
dc.subjectCommon Spatial Patternen_US
dc.subjectEEG Signalsen_US
dc.titleDiagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNNen_US
dc.typeArticleen_US

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