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Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction
Electrocardiogram (ECG) is a record of the heart’s electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872821/ https://www.ncbi.nlm.nih.gov/pubmed/31754217 http://dx.doi.org/10.1038/s41598-019-53460-3 |
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author | Chowdhury, Mehdi Hasan Cheung, Ray C. C. |
author_facet | Chowdhury, Mehdi Hasan Cheung, Ray C. C. |
author_sort | Chowdhury, Mehdi Hasan |
collection | PubMed |
description | Electrocardiogram (ECG) is a record of the heart’s electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq −7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system. |
format | Online Article Text |
id | pubmed-6872821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68728212019-12-04 Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction Chowdhury, Mehdi Hasan Cheung, Ray C. C. Sci Rep Article Electrocardiogram (ECG) is a record of the heart’s electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq −7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system. Nature Publishing Group UK 2019-11-21 /pmc/articles/PMC6872821/ /pubmed/31754217 http://dx.doi.org/10.1038/s41598-019-53460-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chowdhury, Mehdi Hasan Cheung, Ray C. C. Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction |
title | Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction |
title_full | Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction |
title_fullStr | Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction |
title_full_unstemmed | Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction |
title_short | Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction |
title_sort | reconfigurable architecture for multi-lead ecg signal compression with high-frequency noise reduction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872821/ https://www.ncbi.nlm.nih.gov/pubmed/31754217 http://dx.doi.org/10.1038/s41598-019-53460-3 |
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