An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia clas...

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Autores principales:	Mohamed Sraitih, Younes Jabrane, Amir Hajjam El Hassani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electrocardiogram ECG classification support vector machines (SVMs) k-nearest neighbors (kNN) Random Forest (RF) Medicine R
Acceso en línea:	https://doaj.org/article/5a581a25d9a54b3da4418096a2ede394
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spelling	oai:doaj.org-article:5a581a25d9a54b3da4418096a2ede3942021-11-25T18:02:57ZAn Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques10.3390/jcm102254502077-0383https://doaj.org/article/5a581a25d9a54b3da4418096a2ede3942021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/22/5450https://doaj.org/toc/2077-0383The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.Mohamed SraitihYounes JabraneAmir Hajjam El HassaniMDPI AGarticleelectrocardiogramECGclassificationsupport vector machines (SVMs)k-nearest neighbors (kNN)Random Forest (RF)MedicineRENJournal of Clinical Medicine, Vol 10, Iss 5450, p 5450 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electrocardiogram ECG classification support vector machines (SVMs) k-nearest neighbors (kNN) Random Forest (RF) Medicine R
spellingShingle	electrocardiogram ECG classification support vector machines (SVMs) k-nearest neighbors (kNN) Random Forest (RF) Medicine R Mohamed Sraitih Younes Jabrane Amir Hajjam El Hassani An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
description	The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.
format	article
author	Mohamed Sraitih Younes Jabrane Amir Hajjam El Hassani
author_facet	Mohamed Sraitih Younes Jabrane Amir Hajjam El Hassani
author_sort	Mohamed Sraitih
title	An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
title_short	An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
title_full	An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
title_fullStr	An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
title_full_unstemmed	An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
title_sort	automated system for ecg arrhythmia detection using machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5a581a25d9a54b3da4418096a2ede394
work_keys_str_mv	AT mohamedsraitih anautomatedsystemforecgarrhythmiadetectionusingmachinelearningtechniques AT younesjabrane anautomatedsystemforecgarrhythmiadetectionusingmachinelearningtechniques AT amirhajjamelhassani anautomatedsystemforecgarrhythmiadetectionusingmachinelearningtechniques AT mohamedsraitih automatedsystemforecgarrhythmiadetectionusingmachinelearningtechniques AT younesjabrane automatedsystemforecgarrhythmiadetectionusingmachinelearningtechniques AT amirhajjamelhassani automatedsystemforecgarrhythmiadetectionusingmachinelearningtechniques
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An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

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