Wavelets for Electrocardiogram: Overview and Taxonomy
Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbe...
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doaj-82ddfc50a9864506ad7fc4f350a386332021-03-29T22:37:05ZengIEEEIEEE Access2169-35362019-01-017256272564910.1109/ACCESS.2018.28777938528394Wavelets for Electrocardiogram: Overview and TaxonomyWei Li0https://orcid.org/0000-0002-9235-9429School of Instrument Science and Engineering, Southeast University, Nanjing, ChinaPhysiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of “wavelets for ECG” in the future.https://ieeexplore.ieee.org/document/8528394/Wavelets for electrocardiogramsignal denoisingwave detectionheartbeat classificationoverview and taxonomy |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Li |
spellingShingle |
Wei Li Wavelets for Electrocardiogram: Overview and Taxonomy IEEE Access Wavelets for electrocardiogram signal denoising wave detection heartbeat classification overview and taxonomy |
author_facet |
Wei Li |
author_sort |
Wei Li |
title |
Wavelets for Electrocardiogram: Overview and Taxonomy |
title_short |
Wavelets for Electrocardiogram: Overview and Taxonomy |
title_full |
Wavelets for Electrocardiogram: Overview and Taxonomy |
title_fullStr |
Wavelets for Electrocardiogram: Overview and Taxonomy |
title_full_unstemmed |
Wavelets for Electrocardiogram: Overview and Taxonomy |
title_sort |
wavelets for electrocardiogram: overview and taxonomy |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of “wavelets for ECG” in the future. |
topic |
Wavelets for electrocardiogram signal denoising wave detection heartbeat classification overview and taxonomy |
url |
https://ieeexplore.ieee.org/document/8528394/ |
work_keys_str_mv |
AT weili waveletsforelectrocardiogramoverviewandtaxonomy |
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