A novel method for segmentation of QRS complex on ecg signals and classification of cardiovascular diseases via a hybrid model based on machine learning

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dc.contributor.authorSehirli, E.
dc.contributor.authorTuran, M.K.
dc.date.accessioned2024-09-29T16:16:27Z
dc.date.available2024-09-29T16:16:27Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAutomated-detecting intelligent programs and methods are developing to find out diseases in medicine in recent years. Developing new methods and improving existing ones are currently ongoing research. One of the most important health problems is heart diseases for all people in the world. Electrocardiography (ECG) is a diagnosis tool that gives substantially functional information about heart and cardiac system. In this work, it is primarily aimed at developing an intelligent system based on ECG signal processing, analysis, and classification via a hybrid machine learning model. This work uses 837 ECG signal fragments that includes 7 different classes shared in MIT-BIH Arrhythmia database for one lead. The ECG signals are applied on a preprocessing to smooth signals and correct baselines. Q, R and S waves (QRS) complex on ECG signals are segmented based on k-means clustering and tracking local extrema points. Feature extraction and selection are then performed, and a dataset is created by calculating measurement parameters for each QRS points separately. Training sets and test sets based on 8-fold cross validation are generated. A hybrid model based on machine learning models including decision tree (DT), k-nearest neighbor (KNN), random forest (RF), naïve bayes (NB), linear discriminant analysis (LDA), support vector machines (SVM) and quadratic discriminant analysis (QDA) is developed to classify cardiovascular diseases (CVD) into 7 different classes such as normal sinus rhythm (NSR), atrial premature beat (APB), atrial fibrillation (AFIB), premature ventricular contraction (PVC), ventricular bigeminy (VB), left bundle branch block beat (LBBBB) and right bundle branch block beat (RBBBB). Sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) of detection of QRS complex are obtained respectively as 94.75%, 95.96%, 95.57% and 0.90. Sensitivity, specificity, accuracy and MCC of classification of CVD classes are obtained respectively as 92.33%, 92.50%, 92.41%, 0.85. © 2021, Ismail Saritas. All rights reserved.en_US
dc.identifier.doi10.18201/ijisae.2021167932
dc.identifier.endpage21en_US
dc.identifier.issn2147-6799
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85103388238en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage12en_US
dc.identifier.urihttps://doi.org/10.18201/ijisae.2021167932
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9102
dc.identifier.volume9en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIsmail Saritasen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCVDen_US
dc.subjectECG signalen_US
dc.subjectMachine learningen_US
dc.subjectSignal processingen_US
dc.titleA novel method for segmentation of QRS complex on ecg signals and classification of cardiovascular diseases via a hybrid model based on machine learningen_US
dc.typeArticleen_US

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