Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability

Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be use...

Full description

Saved in:
Bibliographic Details
Main Authors: Bruun, Iben H., Hissabu, Semira M. S., Poulsen, Erik S., Puthusserypady, Sadasivan
Format: Conference Proceeding
Language:English
Subjects:
Discrete wavelet transforms
Electrocardiography
Entropy
Feature extraction
Heart rate variability
Rail to rail inputs
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c279t-dda914eaa7c52ad05574ad25d6ebd91bf33e07631a416a6ff25b0f34cbe00fb23
cites
container_end_page 3984
container_issue
container_start_page 3981
container_title
container_volume 2017
creator Bruun, Iben H.
Hissabu, Semira M. S.
Poulsen, Erik S.
Puthusserypady, Sadasivan
description Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a specificity of 99.19%, and an overall accuracy of 98.22%.
doi_str_mv 10.1109/EMBC.2017.8037728
format conference_proceeding
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_miscellaneous_1955063298</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8037728</ieee_id><sourcerecordid>1955063298</sourcerecordid><originalsourceid>FETCH-LOGICAL-c279t-dda914eaa7c52ad05574ad25d6ebd91bf33e07631a416a6ff25b0f34cbe00fb23</originalsourceid><addsrcrecordid>eNotkM1OwzAQhA0CQSl9AMTFRy4payexY26hagGpiAtI3KJN7FCj_BTHKerbY9SedrXzaTSzhNwwmDMG6n75-riYc2BynkEsJc9OyEzJjKWggGeg-CmZcKGSCAQkZ2TC0lRGTMLnJbkahm8AwUTCLsglV4GQQk3ImI--b9HbiubeWWzoypbONk049R3Vxpvqf3ugOe36nWkobreux2pDx8F2X1TboXKBor8YVOOpd9gNde9aip2mG4POU4cB2GHwL21j_f6anNfYDGZ2nFPysVq-L56j9dvTyyJfRxWXykdao2KJQZRVylFDqJOg5qkWptSKlXUcm1AjZpgwgaKueVpCHSdVaQDqksdTcnfwDZF_RjP4og1xTWjXmX4cCqbSFETMVRbQ2wNqjTHF1tkW3b44_jn-A07EcAg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>1955063298</pqid></control><display><type>conference_proceeding</type><title>Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability</title><source>IEEE Xplore All Conference Series</source><creator>Bruun, Iben H. ; Hissabu, Semira M. S. ; Poulsen, Erik S. ; Puthusserypady, Sadasivan</creator><creatorcontrib>Bruun, Iben H. ; Hissabu, Semira M. S. ; Poulsen, Erik S. ; Puthusserypady, Sadasivan</creatorcontrib><description>Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a specificity of 99.19%, and an overall accuracy of 98.22%.</description><identifier>ISSN: 1557-170X</identifier><identifier>EISSN: 2694-0604</identifier><identifier>EISBN: 9781509028092</identifier><identifier>EISBN: 1509028099</identifier><identifier>DOI: 10.1109/EMBC.2017.8037728</identifier><identifier>PMID: 29060769</identifier><language>eng</language><publisher>IEEE</publisher><subject>Discrete wavelet transforms ; Electrocardiography ; Entropy ; Feature extraction ; Heart rate variability ; Rail to rail inputs</subject><ispartof>2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, Vol.2017, p.3981-3984</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c279t-dda914eaa7c52ad05574ad25d6ebd91bf33e07631a416a6ff25b0f34cbe00fb23</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Bruun, Iben H.</creatorcontrib><creatorcontrib>Hissabu, Semira M. S.</creatorcontrib><creatorcontrib>Poulsen, Erik S.</creatorcontrib><creatorcontrib>Puthusserypady, Sadasivan</creatorcontrib><title>Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability</title><title>2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</title><addtitle>EMBC</addtitle><description>Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a specificity of 99.19%, and an overall accuracy of 98.22%.</description><subject>Discrete wavelet transforms</subject><subject>Electrocardiography</subject><subject>Entropy</subject><subject>Feature extraction</subject><subject>Heart rate variability</subject><subject>Rail to rail inputs</subject><issn>1557-170X</issn><issn>2694-0604</issn><isbn>9781509028092</isbn><isbn>1509028099</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2017</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1OwzAQhA0CQSl9AMTFRy4payexY26hagGpiAtI3KJN7FCj_BTHKerbY9SedrXzaTSzhNwwmDMG6n75-riYc2BynkEsJc9OyEzJjKWggGeg-CmZcKGSCAQkZ2TC0lRGTMLnJbkahm8AwUTCLsglV4GQQk3ImI--b9HbiubeWWzoypbONk049R3Vxpvqf3ugOe36nWkobreux2pDx8F2X1TboXKBor8YVOOpd9gNde9aip2mG4POU4cB2GHwL21j_f6anNfYDGZ2nFPysVq-L56j9dvTyyJfRxWXykdao2KJQZRVylFDqJOg5qkWptSKlXUcm1AjZpgwgaKueVpCHSdVaQDqksdTcnfwDZF_RjP4og1xTWjXmX4cCqbSFETMVRbQ2wNqjTHF1tkW3b44_jn-A07EcAg</recordid><startdate>201707</startdate><enddate>201707</enddate><creator>Bruun, Iben H.</creator><creator>Hissabu, Semira M. S.</creator><creator>Poulsen, Erik S.</creator><creator>Puthusserypady, Sadasivan</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>7X8</scope></search><sort><creationdate>201707</creationdate><title>Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability</title><author>Bruun, Iben H. ; Hissabu, Semira M. S. ; Poulsen, Erik S. ; Puthusserypady, Sadasivan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c279t-dda914eaa7c52ad05574ad25d6ebd91bf33e07631a416a6ff25b0f34cbe00fb23</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Discrete wavelet transforms</topic><topic>Electrocardiography</topic><topic>Entropy</topic><topic>Feature extraction</topic><topic>Heart rate variability</topic><topic>Rail to rail inputs</topic><toplevel>online_resources</toplevel><creatorcontrib>Bruun, Iben H.</creatorcontrib><creatorcontrib>Hissabu, Semira M. S.</creatorcontrib><creatorcontrib>Poulsen, Erik S.</creatorcontrib><creatorcontrib>Puthusserypady, Sadasivan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>MEDLINE - Academic</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bruun, Iben H.</au><au>Hissabu, Semira M. S.</au><au>Poulsen, Erik S.</au><au>Puthusserypady, Sadasivan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability</atitle><btitle>2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</btitle><stitle>EMBC</stitle><date>2017-07</date><risdate>2017</risdate><volume>2017</volume><spage>3981</spage><epage>3984</epage><pages>3981-3984</pages><issn>1557-170X</issn><eissn>2694-0604</eissn><eisbn>9781509028092</eisbn><eisbn>1509028099</eisbn><abstract>Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a specificity of 99.19%, and an overall accuracy of 98.22%.</abstract><pub>IEEE</pub><pmid>29060769</pmid><doi>10.1109/EMBC.2017.8037728</doi><tpages>4</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1557-170X
ispartof 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, Vol.2017, p.3981-3984
issn 1557-170X
2694-0604
language eng
recordid cdi_proquest_miscellaneous_1955063298
source IEEE Xplore All Conference Series
subjects Discrete wavelet transforms
Electrocardiography
Entropy
Feature extraction
Heart rate variability
Rail to rail inputs
title Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T00%3A24%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Automatic%20Atrial%20Fibrillation%20detection:%20A%20novel%20approach%20using%20discrete%20wavelet%20transform%20and%20heart%20rate%20variability&rft.btitle=2017%2039th%20Annual%20International%20Conference%20of%20the%20IEEE%20Engineering%20in%20Medicine%20and%20Biology%20Society%20(EMBC)&rft.au=Bruun,%20Iben%20H.&rft.date=2017-07&rft.volume=2017&rft.spage=3981&rft.epage=3984&rft.pages=3981-3984&rft.issn=1557-170X&rft.eissn=2694-0604&rft_id=info:doi/10.1109/EMBC.2017.8037728&rft.eisbn=9781509028092&rft.eisbn_list=1509028099&rft_dat=%3Cproquest_ieee_%3E1955063298%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c279t-dda914eaa7c52ad05574ad25d6ebd91bf33e07631a416a6ff25b0f34cbe00fb23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1955063298&rft_id=info:pmid/29060769&rft_ieee_id=8037728&rfr_iscdi=true