Automated Segmentation of ECG Signals using Piecewise Derivative Dynamic Time Warping

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Automated Segmentation of ECG Signals using Piecewise Derivative Dynamic Time Warping</title> <meta name="description" content="Automated Segmentation of ECG Signals using Piecewise Derivative Dynamic Time Warping"> <meta name="keywords" content="Adaptive Piecewise Constant Approximation,Dynamic programming, ECG segmentation, Piecewise DerivativeDynamic Time Warping."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Automated Segmentation of ECG Signals using Piecewise Derivative Dynamic Time Warping"> <meta name="citation_author" content="Ali Zifan"> <meta name="citation_author" content="Mohammad Hassan Moradi"> <meta name="citation_author" content="Sohrab Saberi"> <meta name="citation_author" content="Farzad Towhidkhah"> <meta name="citation_publication_date" content="2008/08/25"> <meta name="citation_journal_title" content="International Journal of Biomedical and Biological 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class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ali%20Zifan">Ali Zifan</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Hassan%20Moradi"> Mohammad Hassan Moradi</a>, <a href="https://publications.waset.org/search?q=Sohrab%20Saberi"> Sohrab Saberi</a>, <a href="https://publications.waset.org/search?q=Farzad%20Towhidkhah"> Farzad Towhidkhah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG-s. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW). The results are tested on the QT database. We compared our results to Laguna-s two lead method. Our proposed approach has a comparable mean error, but yields a slightly higher standard deviation than Laguna-s method. <iframe src="https://publications.waset.org/8423.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptive%20Piecewise%20Constant%20Approximation" title="Adaptive Piecewise Constant Approximation">Adaptive Piecewise Constant Approximation</a>, <a href="https://publications.waset.org/search?q=Dynamic%20programming" title="Dynamic programming">Dynamic programming</a>, <a href="https://publications.waset.org/search?q=ECG%20segmentation" title=" ECG segmentation"> ECG segmentation</a>, <a href="https://publications.waset.org/search?q=Piecewise%20DerivativeDynamic%20Time%20Warping." title=" Piecewise DerivativeDynamic Time Warping."> Piecewise DerivativeDynamic Time Warping.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1070865" target="_blank">doi.org/10.5281/zenodo.1070865</a> </p> <a href="https://publications.waset.org/8423/automated-segmentation-of-ecg-signals-using-piecewise-derivative-dynamic-time-warping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8423/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8423/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8423/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8423/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8423/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8423/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8423/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8423/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8423/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8423/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8423.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2067</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani: "Locally adaptive dimensionality reduction for indexing large time series databases." ACM Trans. Database Syst. 27: 188-228 ,2002. <br>[2] P. Laguna, R. JanB, and P. Caminal, "Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database," Computers and biomedical research, vol. 27, pp. 45-60, 1994. <br>[3] J.S. Sahambi, S.N. Tandon, and R.K.P. Bhatt, "Using wavelet transform for ECG characterization," IEEE Eng. in Med. and Biol., vol. 16, no. 1, pp. 77-83, 1997. <br>[4] I.S.N. Murthy and U.C. Niranjan , "Component wave delineation of ECG by filtering in the fourier domain," Medical &biological Eng. computing, vol. 30, pp. 169-176, 1992. <br>[5] H.J.L.M Vullings , M. H. G. Verhaegen , ,H. B. Verbruggen ," Automated ECG segmentation with Dynamic Time Warping " , IEEE engineering conference in Medicine and Biology society,Vol.20,No.1, 1998. <br>[6] L. Clavier and J.M. Boucher, "Segmentation of electrocardiograms using a Hidden Markov Model," in Bridging Disciplines for Biomedicine. IEEE Eng. in Med. and Biol., 1996.pp. 45-60, 1994. <br>[7] S. Graja and J. M. Boucher. "Multiscale hidden Markov model applied to ECG segmentation". In WISP 2003: IEEE International Symposium on Intelligent Signal Processing, pages 105-109, Budapest, Hungary, 2003. <br>[8] Crouse M.s.,Nowak R.d,baraniuk R.G,"wavelet based statistical signal processing using hidden markov models",IEEE Trans.on Biomedical engineering Vol.37,p826-836,1990. <br>[9] Nicholas P. Hughes, Stephen J. Roberts and Lionel Tarassenko," Semi- Supervised Learning of Probabilistic Models for ECG Segmentation ", IEEE Engineering in Medicine and Biology Conference (EMBC), 2004. <br>[10] Nicholas P. Hughes, Lionel Tarassenko and Stephen J. Roberts," Markov Models for Automated ECG Interval Analysis", In Advances in Neural Information Processing Systems 16 ,NIPS, 2003. <br>[11] P. Laguna, R. Mark, A. Goldberger, and G.B. Moody, "A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG.," in Computers in Cardiology, 1997. <br>[12] Pan J, Tompkins, WJ. "A Real-Time QRS Detection Algorithm", IEEE Transactions on Biomedical Engineering, 230-23, 1985. <br>[13] Eamonn J. Keogh and Michael J. Pazzani, "Derivative Dynamic Time warping", SDM conference, Chicago, USA,2001. <br>[14] Eamonn J. Keogh, Michael J. Pazzani," Scaling up Dynamic Time Warping for Datamining." Proceedings of the KDD conference on Knowledge discovery and data mining,pp.285-289,2000. <br>[15] Zywietz C. , Celikag D. , "Testing Results and Derivation of Minimum Performance criteria for Computerized ECG-Analysis.",Computers in Cardiology , 97-100,1991. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Automated Segmentation of ECG Signals using Piecewise Derivative Dynamic Time Warping