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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports 2019-11, Vol.9 (1), p.17233-12, Article 17233
Hauptverfasser: Chowdhury, Mehdi Hasan, Cheung, Ray C. C.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 12
container_issue 1
container_start_page 17233
container_title Scientific reports
container_volume 9
creator Chowdhury, Mehdi Hasan
Cheung, Ray C. C.
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.
doi_str_mv 10.1038/s41598-019-53460-3
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6872821</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2317595487</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-e8175c88546ba42fe64a6f1fac465d8cdb10d0eddf288ed165b937044d9bd28c3</originalsourceid><addsrcrecordid>eNp9kc9PFTEQxxujEYL8Ax5MEy9eiv25272YkBcEE5QE9dx02-m-kn3bZ7uL4b-n8BDRA3PpJPOZb2fmi9BbRo8YFfpjkUx1mlDWESVkQ4l4gfY5lYpwwfnLJ_keOizlitZQvJOse432BGuV5KzdR3AJLk0hDku2_Qj4OLt1nMHNSwYcUsZfl3GOZATr8cnqFH-Pw2RHvEqbbYZSYprw7ziv8Vkc1iRk-LXA5G7wtxQL4Evwi5sr8wa9CnYscPjwHqCfn09-rM7I-cXpl9XxOXGylTMBXedyWivZ9FbyAI20TWDBOtkor53vGfUUvA9ca_CsUX0nWiql73rPtRMH6NNOd7v0G_AOpjnb0Wxz3Nh8Y5KN5t_KFNdmSNem0S3XnFWBDw8COdVVymw2sTgYRztBWorhd5frlNRtRd__h16lJdfj3FNN23QtpZXiO8rlVEqG8DgMo-bOSLMz0lQjzb2RRtSmd0_XeGz5Y1sFxA4otTQNkP_-_YzsLfGpqnY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2316769700</pqid></control><display><type>article</type><title>Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction</title><source>MEDLINE</source><source>Nature Free</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><source>Free Full-Text Journals in Chemistry</source><source>Springer Nature OA Free Journals</source><creator>Chowdhury, Mehdi Hasan ; Cheung, Ray C. C.</creator><creatorcontrib>Chowdhury, Mehdi Hasan ; Cheung, Ray C. C.</creatorcontrib><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.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-019-53460-3</identifier><identifier>PMID: 31754217</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>639/166/985 ; 639/166/987 ; Algorithms ; Compression ; Computers ; Coronary artery disease ; Data Compression - methods ; EKG ; Electrocardiography ; Electrocardiography - methods ; Field programmable gate arrays ; Fourier Analysis ; Heart diseases ; Humanities and Social Sciences ; multidisciplinary ; Noise reduction ; Science ; Science (multidisciplinary) ; Signal processing ; Signal Processing, Computer-Assisted ; Software</subject><ispartof>Scientific reports, 2019-11, Vol.9 (1), p.17233-12, Article 17233</ispartof><rights>The Author(s) 2019</rights><rights>2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-e8175c88546ba42fe64a6f1fac465d8cdb10d0eddf288ed165b937044d9bd28c3</citedby><cites>FETCH-LOGICAL-c474t-e8175c88546ba42fe64a6f1fac465d8cdb10d0eddf288ed165b937044d9bd28c3</cites><orcidid>0000-0002-7645-8443</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872821/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872821/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31754217$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chowdhury, Mehdi Hasan</creatorcontrib><creatorcontrib>Cheung, Ray C. C.</creatorcontrib><title>Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><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.</description><subject>639/166/985</subject><subject>639/166/987</subject><subject>Algorithms</subject><subject>Compression</subject><subject>Computers</subject><subject>Coronary artery disease</subject><subject>Data Compression - methods</subject><subject>EKG</subject><subject>Electrocardiography</subject><subject>Electrocardiography - methods</subject><subject>Field programmable gate arrays</subject><subject>Fourier Analysis</subject><subject>Heart diseases</subject><subject>Humanities and Social Sciences</subject><subject>multidisciplinary</subject><subject>Noise reduction</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Software</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kc9PFTEQxxujEYL8Ax5MEy9eiv25272YkBcEE5QE9dx02-m-kn3bZ7uL4b-n8BDRA3PpJPOZb2fmi9BbRo8YFfpjkUx1mlDWESVkQ4l4gfY5lYpwwfnLJ_keOizlitZQvJOse432BGuV5KzdR3AJLk0hDku2_Qj4OLt1nMHNSwYcUsZfl3GOZATr8cnqFH-Pw2RHvEqbbYZSYprw7ziv8Vkc1iRk-LXA5G7wtxQL4Evwi5sr8wa9CnYscPjwHqCfn09-rM7I-cXpl9XxOXGylTMBXedyWivZ9FbyAI20TWDBOtkor53vGfUUvA9ca_CsUX0nWiql73rPtRMH6NNOd7v0G_AOpjnb0Wxz3Nh8Y5KN5t_KFNdmSNem0S3XnFWBDw8COdVVymw2sTgYRztBWorhd5frlNRtRd__h16lJdfj3FNN23QtpZXiO8rlVEqG8DgMo-bOSLMz0lQjzb2RRtSmd0_XeGz5Y1sFxA4otTQNkP_-_YzsLfGpqnY</recordid><startdate>20191121</startdate><enddate>20191121</enddate><creator>Chowdhury, Mehdi Hasan</creator><creator>Cheung, Ray C. C.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7645-8443</orcidid></search><sort><creationdate>20191121</creationdate><title>Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction</title><author>Chowdhury, Mehdi Hasan ; Cheung, Ray C. C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-e8175c88546ba42fe64a6f1fac465d8cdb10d0eddf288ed165b937044d9bd28c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>639/166/985</topic><topic>639/166/987</topic><topic>Algorithms</topic><topic>Compression</topic><topic>Computers</topic><topic>Coronary artery disease</topic><topic>Data Compression - methods</topic><topic>EKG</topic><topic>Electrocardiography</topic><topic>Electrocardiography - methods</topic><topic>Field programmable gate arrays</topic><topic>Fourier Analysis</topic><topic>Heart diseases</topic><topic>Humanities and Social Sciences</topic><topic>multidisciplinary</topic><topic>Noise reduction</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Signal processing</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chowdhury, Mehdi Hasan</creatorcontrib><creatorcontrib>Cheung, Ray C. C.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chowdhury, Mehdi Hasan</au><au>Cheung, Ray C. C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2019-11-21</date><risdate>2019</risdate><volume>9</volume><issue>1</issue><spage>17233</spage><epage>12</epage><pages>17233-12</pages><artnum>17233</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>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.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>31754217</pmid><doi>10.1038/s41598-019-53460-3</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7645-8443</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2045-2322
ispartof Scientific reports, 2019-11, Vol.9 (1), p.17233-12, Article 17233
issn 2045-2322
2045-2322
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6872821
source MEDLINE; Nature Free; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry; Springer Nature OA Free Journals
subjects 639/166/985
639/166/987
Algorithms
Compression
Computers
Coronary artery disease
Data Compression - methods
EKG
Electrocardiography
Electrocardiography - methods
Field programmable gate arrays
Fourier Analysis
Heart diseases
Humanities and Social Sciences
multidisciplinary
Noise reduction
Science
Science (multidisciplinary)
Signal processing
Signal Processing, Computer-Assisted
Software
title Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T16%3A44%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reconfigurable%20Architecture%20for%20Multi-lead%20ECG%20Signal%20Compression%20with%20High-frequency%20Noise%20Reduction&rft.jtitle=Scientific%20reports&rft.au=Chowdhury,%20Mehdi%20Hasan&rft.date=2019-11-21&rft.volume=9&rft.issue=1&rft.spage=17233&rft.epage=12&rft.pages=17233-12&rft.artnum=17233&rft.issn=2045-2322&rft.eissn=2045-2322&rft_id=info:doi/10.1038/s41598-019-53460-3&rft_dat=%3Cproquest_pubme%3E2317595487%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2316769700&rft_id=info:pmid/31754217&rfr_iscdi=true