Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents hi...

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Autor principal:	Pedro Henrique Borghi de Melo
Formato:	article
Lenguaje:	EN
Publicado:	Universidade do Porto 2021
Materias:	ecg analysis ecg classification machine learning deep learning biomedical signal processing feature processing Engineering (General). Civil engineering (General) TA1-2040 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/7d497a43ba69486587029fb7395890fe
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spelling	oai:doaj.org-article:7d497a43ba69486587029fb7395890fe2021-11-26T12:34:56ZBrief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches2183-649310.24840/2183-6493_007.004_0012https://doaj.org/article/7d497a43ba69486587029fb7395890fe2021-11-01T00:00:00Zhttps://journalengineering.fe.up.pt/index.php/upjeng/article/view/917https://doaj.org/toc/2183-6493Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.Pedro Henrique Borghi de MeloUniversidade do Portoarticleecg analysisecg classificationmachine learningdeep learningbiomedical signal processingfeature processingEngineering (General). Civil engineering (General)TA1-2040Technology (General)T1-995ENU.Porto Journal of Engineering, Vol 7, Iss 4, Pp 153-162 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ecg analysis ecg classification machine learning deep learning biomedical signal processing feature processing Engineering (General). Civil engineering (General) TA1-2040 Technology (General) T1-995
spellingShingle	ecg analysis ecg classification machine learning deep learning biomedical signal processing feature processing Engineering (General). Civil engineering (General) TA1-2040 Technology (General) T1-995 Pedro Henrique Borghi de Melo Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches
description	Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.
format	article
author	Pedro Henrique Borghi de Melo
author_facet	Pedro Henrique Borghi de Melo
author_sort	Pedro Henrique Borghi de Melo
title	Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches
title_short	Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches
title_full	Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches
title_fullStr	Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches
title_full_unstemmed	Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches
title_sort	brief review on electrocardiogram analysis and classification techniques with machine learning approaches
publisher	Universidade do Porto
publishDate	2021
url	https://doaj.org/article/7d497a43ba69486587029fb7395890fe
work_keys_str_mv	AT pedrohenriqueborghidemelo briefreviewonelectrocardiogramanalysisandclassificationtechniqueswithmachinelearningapproaches
_version_	1718409421206323200

Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

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