Yıl: 2021 Cilt: 9 Sayı: 1 Sayfa Aralığı: 12 - 21 Metin Dili: İngilizce İndeks Tarihi: 20-05-2021

A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning

Öz:
Automated-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 heartdiseases for all people in the world. Electrocardiography (ECG) is a diagnosis tool that gives substantially functional information aboutheart 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 sharedin 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. Featureextraction 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 includingdecision tree (DT), k-nearest neighbor (KNN), random forest (RF), naïve bayes (NB), linear discriminant analysis (LDA), support vectormachines (SVM) and quadratic discriminant analysis (QDA) is developed to classify cardiovascular diseases (CVD) into 7 different classessuch 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.
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APA Şehirli E, Turan M (2021). A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. , 12 - 21.
Chicago Şehirli Eftal,Turan Muhammed Kamil A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. (2021): 12 - 21.
MLA Şehirli Eftal,Turan Muhammed Kamil A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. , 2021, ss.12 - 21.
AMA Şehirli E,Turan M A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. . 2021; 12 - 21.
Vancouver Şehirli E,Turan M A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. . 2021; 12 - 21.
IEEE Şehirli E,Turan M "A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning." , ss.12 - 21, 2021.
ISNAD Şehirli, Eftal - Turan, Muhammed Kamil. "A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning". (2021), 12-21.
APA Şehirli E, Turan M (2021). A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 9(1), 12 - 21.
Chicago Şehirli Eftal,Turan Muhammed Kamil A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering 9, no.1 (2021): 12 - 21.
MLA Şehirli Eftal,Turan Muhammed Kamil A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, vol.9, no.1, 2021, ss.12 - 21.
AMA Şehirli E,Turan M A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering. 2021; 9(1): 12 - 21.
Vancouver Şehirli E,Turan M A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering. 2021; 9(1): 12 - 21.
IEEE Şehirli E,Turan M "A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning." International Journal of Intelligent Systems and Applications in Engineering, 9, ss.12 - 21, 2021.
ISNAD Şehirli, Eftal - Turan, Muhammed Kamil. "A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning". International Journal of Intelligent Systems and Applications in Engineering 9/1 (2021), 12-21.