ECG-based machine-learning algorithms for heartbeat classification

Abstract Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG...

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Autores principales:	Saira Aziz, Sajid Ahmed, Mohamed-Slim Alouini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c14651eee2534ba9bbfcd09b77080861
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spelling	oai:doaj.org-article:c14651eee2534ba9bbfcd09b770808612021-12-02T18:14:30ZECG-based machine-learning algorithms for heartbeat classification10.1038/s41598-021-97118-52045-2322https://doaj.org/article/c14651eee2534ba9bbfcd09b770808612021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97118-5https://doaj.org/toc/2045-2322Abstract Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.Saira AzizSajid AhmedMohamed-Slim AlouiniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saira Aziz Sajid Ahmed Mohamed-Slim Alouini ECG-based machine-learning algorithms for heartbeat classification
description	Abstract Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.
format	article
author	Saira Aziz Sajid Ahmed Mohamed-Slim Alouini
author_facet	Saira Aziz Sajid Ahmed Mohamed-Slim Alouini
author_sort	Saira Aziz
title	ECG-based machine-learning algorithms for heartbeat classification
title_short	ECG-based machine-learning algorithms for heartbeat classification
title_full	ECG-based machine-learning algorithms for heartbeat classification
title_fullStr	ECG-based machine-learning algorithms for heartbeat classification
title_full_unstemmed	ECG-based machine-learning algorithms for heartbeat classification
title_sort	ecg-based machine-learning algorithms for heartbeat classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c14651eee2534ba9bbfcd09b77080861
work_keys_str_mv	AT sairaaziz ecgbasedmachinelearningalgorithmsforheartbeatclassification AT sajidahmed ecgbasedmachinelearningalgorithmsforheartbeatclassification AT mohamedslimalouini ecgbasedmachinelearningalgorithmsforheartbeatclassification
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ECG-based machine-learning algorithms for heartbeat classification

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