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Advanced Time-Frequency Methods for ECG Waves Recognition

ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required...

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Autores principales: Zyout, Ala’a, Alquran, Hiam, Mustafa, Wan Azani, Alqudah, Ali Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858079/
https://www.ncbi.nlm.nih.gov/pubmed/36673118
http://dx.doi.org/10.3390/diagnostics13020308
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author Zyout, Ala’a
Alquran, Hiam
Mustafa, Wan Azani
Alqudah, Ali Mohammad
author_facet Zyout, Ala’a
Alquran, Hiam
Mustafa, Wan Azani
Alqudah, Ali Mohammad
author_sort Zyout, Ala’a
collection PubMed
description ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required to extract representative features. This paper studies the performance of two different spectrum representations, iris-spectrogram and scalogram, for different ECG beat waves in terms of recognition of normal, tachycardia, and bradycardia classes. These two different spectra are then sent to two different deep convolutional neural networks (CNN), i.e., Resnet101 and ShuffleNet, for deep feature extraction and classification. The results show that the best accuracy for detection of beats rhythm was using ResNet101 and scalogram of T-wave with an accuracy of 98.3%, while accuracy was 94.4% for detection using iris-spectrogram using also ResNet101 and QRS-Wave. Finally, based on these results we note that using deep features from time-frequency representation using one wave of ECG beat we can accurately detect basic rhythms such as normal, tachycardia, and bradycardia.
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spelling pubmed-98580792023-01-21 Advanced Time-Frequency Methods for ECG Waves Recognition Zyout, Ala’a Alquran, Hiam Mustafa, Wan Azani Alqudah, Ali Mohammad Diagnostics (Basel) Article ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required to extract representative features. This paper studies the performance of two different spectrum representations, iris-spectrogram and scalogram, for different ECG beat waves in terms of recognition of normal, tachycardia, and bradycardia classes. These two different spectra are then sent to two different deep convolutional neural networks (CNN), i.e., Resnet101 and ShuffleNet, for deep feature extraction and classification. The results show that the best accuracy for detection of beats rhythm was using ResNet101 and scalogram of T-wave with an accuracy of 98.3%, while accuracy was 94.4% for detection using iris-spectrogram using also ResNet101 and QRS-Wave. Finally, based on these results we note that using deep features from time-frequency representation using one wave of ECG beat we can accurately detect basic rhythms such as normal, tachycardia, and bradycardia. MDPI 2023-01-13 /pmc/articles/PMC9858079/ /pubmed/36673118 http://dx.doi.org/10.3390/diagnostics13020308 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zyout, Ala’a
Alquran, Hiam
Mustafa, Wan Azani
Alqudah, Ali Mohammad
Advanced Time-Frequency Methods for ECG Waves Recognition
title Advanced Time-Frequency Methods for ECG Waves Recognition
title_full Advanced Time-Frequency Methods for ECG Waves Recognition
title_fullStr Advanced Time-Frequency Methods for ECG Waves Recognition
title_full_unstemmed Advanced Time-Frequency Methods for ECG Waves Recognition
title_short Advanced Time-Frequency Methods for ECG Waves Recognition
title_sort advanced time-frequency methods for ecg waves recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858079/
https://www.ncbi.nlm.nih.gov/pubmed/36673118
http://dx.doi.org/10.3390/diagnostics13020308
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