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On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications
This paper presents a compression study of electrocardiogram (ECG) signals for e-Health cardiac online diagnostic systems. The study uses 75 real electrocardiogram records sampled with continuous-time level-crossing (LC) analog-to-digital converter (ADC). This signal-dependent LC-ADC compresses sign...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7255352/ https://www.ncbi.nlm.nih.gov/pubmed/32565605 http://dx.doi.org/10.1016/j.measurement.2020.108031 |
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author | Maalej, Asma Ben-Romdhane, Manel Tlili, Mariam Rivet, François Dallet, Dominique Rebai, Chiheb |
author_facet | Maalej, Asma Ben-Romdhane, Manel Tlili, Mariam Rivet, François Dallet, Dominique Rebai, Chiheb |
author_sort | Maalej, Asma |
collection | PubMed |
description | This paper presents a compression study of electrocardiogram (ECG) signals for e-Health cardiac online diagnostic systems. The study uses 75 real electrocardiogram records sampled with continuous-time level-crossing (LC) analog-to-digital converter (ADC). This signal-dependent LC-ADC compresses signals compared to conventional ADC but further compression is needed especially for long-time monitoring applications. The orthogonal matching pursuit algorithm is simulated to evaluate ECG compression with 54 orthogonal and biorthogonal wavelets. For LC-ADC amplitude output compression, Biorthogonal3.1 (bior3.1) wavelet achieves optimal performances in terms of compression ratio (CR) while ensuring 2-% percentage root-mean-square difference (PRD). The PRD must be limited to this value to ensure a very good quality signals after decompression. For circuit implementation purposes, bior3.1 wavelet is proposed as a multiplier-free decomposition step and a noncomplex global and hard thresholding process is achieved. The average CR is 63% and PRD varies between 0.1 and 2.1% leading to a very good diagnostic quality. |
format | Online Article Text |
id | pubmed-7255352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72553522020-05-28 On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications Maalej, Asma Ben-Romdhane, Manel Tlili, Mariam Rivet, François Dallet, Dominique Rebai, Chiheb Measurement (Lond) Article This paper presents a compression study of electrocardiogram (ECG) signals for e-Health cardiac online diagnostic systems. The study uses 75 real electrocardiogram records sampled with continuous-time level-crossing (LC) analog-to-digital converter (ADC). This signal-dependent LC-ADC compresses signals compared to conventional ADC but further compression is needed especially for long-time monitoring applications. The orthogonal matching pursuit algorithm is simulated to evaluate ECG compression with 54 orthogonal and biorthogonal wavelets. For LC-ADC amplitude output compression, Biorthogonal3.1 (bior3.1) wavelet achieves optimal performances in terms of compression ratio (CR) while ensuring 2-% percentage root-mean-square difference (PRD). The PRD must be limited to this value to ensure a very good quality signals after decompression. For circuit implementation purposes, bior3.1 wavelet is proposed as a multiplier-free decomposition step and a noncomplex global and hard thresholding process is achieved. The average CR is 63% and PRD varies between 0.1 and 2.1% leading to a very good diagnostic quality. Elsevier Ltd. 2020-11 2020-05-27 /pmc/articles/PMC7255352/ /pubmed/32565605 http://dx.doi.org/10.1016/j.measurement.2020.108031 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Maalej, Asma Ben-Romdhane, Manel Tlili, Mariam Rivet, François Dallet, Dominique Rebai, Chiheb On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications |
title | On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications |
title_full | On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications |
title_fullStr | On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications |
title_full_unstemmed | On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications |
title_short | On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications |
title_sort | on the wavelet-based compressibility of continuous-time sampled ecg signal for e-health applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7255352/ https://www.ncbi.nlm.nih.gov/pubmed/32565605 http://dx.doi.org/10.1016/j.measurement.2020.108031 |
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