{"id":71080,"date":"2024-10-15T15:46:09","date_gmt":"2024-10-15T13:46:09","guid":{"rendered":"https:\/\/www.iemn.fr\/?p=71080"},"modified":"2024-10-15T15:46:09","modified_gmt":"2024-10-15T13:46:09","slug":"these-capteurs-a-ondes-acoustiques-de-surface-pour-la-caracterisation-multiphysique-des-proprietes-des-toles-ferromagnetiques-dans-les-machines-electriques-de-fortes-puissances-2","status":"publish","type":"post","link":"https:\/\/www.iemn.fr\/en\/a-la-une\/these-capteurs-a-ondes-acoustiques-de-surface-pour-la-caracterisation-multiphysique-des-proprietes-des-toles-ferromagnetiques-dans-les-machines-electriques-de-fortes-puissances-2.html","title":{"rendered":"Thesis: Julien DUFOREST \"Classification of cardiac arrhythmias using antidictionnaires in event mode\"."},"content":{"rendered":"<div id='layer_slider_1'  class='avia-layerslider main_color avia-shadow  avia-builder-el-0  el_before_av_heading  avia-builder-el-first  container_wrap sidebar_right'  style='height: 261px;'  ><div id=\"layerslider_58_4y7jenlb9b3l\" data-ls-slug=\"homepageslider\" class=\"ls-wp-container fitvidsignore ls-selectable\" style=\"width:1140px;height:260px;margin:0 auto;margin-bottom: 0px;\"><div class=\"ls-slide\" data-ls=\"duration:6000;transition2d:5;\"><img loading=\"lazy\" decoding=\"async\" width=\"2600\" height=\"270\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg\" class=\"ls-bg\" alt=\"\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg 2600w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-300x31.jpg 300w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-768x80.jpg 768w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1030x107.jpg 1030w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1500x156.jpg 1500w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-705x73.jpg 705w\" sizes=\"auto, (max-width: 2600px) 100vw, 2600px\" \/><ls-layer style=\"font-size:14px;text-align:left;font-style:normal;text-decoration:none;text-transform:none;font-weight:700;letter-spacing:0px;border-style:solid;border-color:#000;background-position:0% 0%;background-repeat:no-repeat;width:180px;height:30px;left:0px;top:231px;line-height:32px;color:#ffffff;border-radius:6px 6px 6px 6px;padding-left:50px;background-color:rgba(0, 0, 0, 0.57);\" class=\"ls-l ls-ib-icon ls-text-layer\" data-ls=\"minfontsize:0;minmobilefontsize:0;\"><i class=\"fa fa-quote-right\" style=\"color:#ffffff;margin-right:0.8em;font-size:1em;transform:translateY( -0.125em );\"><\/i>ACTUALITES<\/ls-layer><\/div><\/div><\/div><div id='after_layer_slider_1'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-71080'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-k5dohoxw-b0b3a09ceb5b3cdc7edaec9363bcc552\">\n#top .av-special-heading.av-k5dohoxw-b0b3a09ceb5b3cdc7edaec9363bcc552{\nmargin:0 0 10px 0;\npadding-bottom:4px;\n}\nbody .av-special-heading.av-k5dohoxw-b0b3a09ceb5b3cdc7edaec9363bcc552 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-k5dohoxw-b0b3a09ceb5b3cdc7edaec9363bcc552 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-k5dohoxw-b0b3a09ceb5b3cdc7edaec9363bcc552 av-special-heading-h4  avia-builder-el-1  el_after_av_layerslider  el_before_av_hr  avia-builder-el-first  av-linked-heading'><h4 class='av-special-heading-tag'  itemprop=\"headline\"  >Thesis: Julien DUFOREST \"Classification of cardiac arrhythmias using antidictionnaires in event mode\".<\/h4><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-18u73nj-dad6a947580930e400fc42ba200e80f1\">\n#top .hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1{\nmargin-top:5px;\nmargin-bottom:5px;\n}\n.hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1 .hr-inner{\nwidth:100%;\n}\n<\/style>\n<div  class='hr av-18u73nj-dad6a947580930e400fc42ba200e80f1 hr-custom  avia-builder-el-2  el_after_av_heading  el_before_av_textblock  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-thin'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<section  class='av_textblock_section av-jriy64i8-fd5f2e9d63bf552d6910d12f255eb26e'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-13ewzjw-68e036126b913e5028f77311dc66b825\">\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825{\ncolor:#bfbfbf;\nborder-color:#bfbfbf;\n}\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825 .av-icon-char{\nfont-size:60px;\nline-height:60px;\n}\n<\/style>\n<span  class='av_font_icon av-13ewzjw-68e036126b913e5028f77311dc66b825 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue8c9' data-av_iconfont='entypo-fontello' ><\/span><\/span>\n<h6>Julien DUFOREST<\/h6>\n<p><strong>\u00a0<\/strong><\/p>\n<p>Oral defence: 14 October 2024 at 2pm <strong><br \/>\n<\/strong>Junia Lille<\/p>\n<\/div><\/section>\n<section  class='av_textblock_section av-jtefqx33-26fa44c544b10063385818eda37e027b'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><div  class='hr av-kjh3zw-4dff888f744b728a1aca9b3a0971493a hr-default  avia-builder-el-6  avia-builder-el-no-sibling'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<h5><strong><span style=\"color: #800000;\">Jury :<\/span><\/strong><\/h5>\n<table>\n<tbody>\n<tr>\n<td>Dominique DALLET<\/td>\n<td><\/td>\n<td>University Professor<\/td>\n<td><\/td>\n<td>Bordeaux INP<\/td>\n<td><\/td>\n<td>Rapporteur<\/td>\n<\/tr>\n<tr>\n<td>Marina REYBOZ<\/td>\n<td><\/td>\n<td>Research Director<\/td>\n<td><\/td>\n<td>CEA-Leti Grenoble<\/td>\n<td><\/td>\n<td>Rapporteur<\/td>\n<\/tr>\n<tr>\n<td>Laurent CLAVIER<\/td>\n<td><\/td>\n<td>Professor<\/td>\n<td><\/td>\n<td>IMT Nord Europe<\/td>\n<td><\/td>\n<td>Examinateur<\/td>\n<\/tr>\n<tr>\n<td>Cyril LAHUEC<\/td>\n<td><\/td>\n<td>Professor<\/td>\n<td><\/td>\n<td>ITM Atlantic<\/td>\n<td><\/td>\n<td>Examinateur<\/td>\n<\/tr>\n<tr>\n<td>Benoit LARRAS<\/td>\n<td><\/td>\n<td>Lecturer and researcher (ENAC, ISAE)<\/td>\n<td><\/td>\n<td>JUNIA<\/td>\n<td><\/td>\n<td>Examinateur<\/td>\n<\/tr>\n<tr>\n<td>Antoine FRAPP\u00e9<\/td>\n<td><\/td>\n<td>Lecturer and researcher (ENAC, ISAE)<\/td>\n<td><\/td>\n<td>Universit\u00e9 de Lille<\/td>\n<td><\/td>\n<td>Directeur de th\u00e8se<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h5><\/h5>\n<h5><\/h5>\n<h5>Summary:<\/h5>\n<p>ardiovascular disease is the leading cause of death in the world, accounting for 31.4% of deaths worldwide in 2022. With more than half of cardiovascular disease-related deaths occurring outside hospital, there is a need for a constant home monitoring system. These monitoring systems need to be autonomous, comfortable and consume little energy. Since electrocardigram signals are by nature sparse events over time, a non-uniform quantization scheme is suitable for encoding this signal. This allows data-dependent energy consumption to be concentrated during periods of activity. In this context, this thesis studies the features used to differentiate between arrhythmic and healthy heartbeats used in cardiology. Particular emphasis is placed on the implementation of the feature extraction process in a<br \/>\nevent-driven system in order to take advantage of the sparse nature of the signal. In addition, a study of integrated and non-integrated classification methods is presented, focusing on the search for ways to implement artificial intelligence directly in the system.<br \/>\nCombining an event-based sampling scheme with on-chip artificial intelligence (AI) thus aims to achieve a system complexity several orders of magnitude lower than that of state-of-the-art systems. Most event-driven systems use time-related features to classify arrhythmias, but they often require a sampling clock, which runs counter to the advantages of event-driven systems. In this thesis, we investigate a system in which the temporal information contained in sampled input data is extracted without a regular clock, using successive delays that coarsely encode the time between event-driven samples.<br \/>\nUsing the delay output and the output of a Level-Crossing Analog to Digital Converter, an event-driven token is generated for each sample, containing coarse information about the shape of the ECG signal. Using these tokens as input to a neural network, an average accuracy of 98% can be obtained using the MIT-BIH database. However, as training a neural network with unbalanced datasets introduces bias, a new classification system based on antidictionaries is introduced. It consists of models created from negative information on healthy heartbeats, enabling the detection of arrhythmic heartbeats by training systems based on the same information.<br \/>\nbased on AI without introducing bias. Classification based on antidictionaries achieves a similar average accuracy of 98 %, demonstrating its ability to classify arrhythmias at a level equivalent to state-of-the-art neural networks. The FPGA-based system requires 99% less memory to store the classifier, 1,024 logical comparisons instead of 512 multiplication operations and 99% less accumulation compared with the state of the art. This translates into 25.1% less Look-Up Table and 34.5% less registers used on the FPGA compared to the state of the art and does not require the implementation of any RAM. As a result of these advantages, the overall classification accuracy has been reduced by 1.03 percentage points compared with the state-of-the-art, with a classification accuracy of 97.97%.<\/p>\n<h5>Abstract:<\/h5>\n<p>Cardiovascular diseases are the leading cause of death globally, representing 31.4% of all deaths in the world in 2022. With more than half of all cardiovascular disease-related deaths happening in settings outside the hospital, there is a need<br \/>\nfor constant at-home monitoring to prevent them. Such long-term monitoring solutions necessitate autonomous, comfortable, and low-power consumption devices. Since electrocardiogram signals are constituted by nature by sparse events in time, a non-uniform<br \/>\nquantization scheme is well suited to code this signal. It will enable a data-dependent power consumption concentrated during the activity periods.<br \/>\nIn this context, this thesis investigates features used to differentiate arrhythmic and healthy heartbeats used in cardiology. A particular focus is made on the implementation of the feature extraction process in an event-driven system to benefit from the sparse nature of the signal. In addition, a study of classification methods both integrated and non-integrated is presented focusing on finding ways to implement artificial intelligence directly in the system.<br \/>\nCombining an event-driven sampling scheme with on-chip artificial intelligence (AI) thus aims to achieve a system complexity that is lower by several orders of magnitude than state-of-the-art systems. Most event-driven systems utilize time-related features to clas-<br \/>\nsify arrhythmia, but they often require a sampling clock, which counteracts the benefits of event-driven systems.<br \/>\nIn this thesis, we investigate a system where the time information contained in the sampled input data is extracted without a regular clock, using successive delays coarsely coding the time between event-driven samples.<br \/>\nUsing the output of the delays and the output of a Level-Crossing Analog-to-Digital Converter, an event-driven token is generated for each sample, holding coarse information on the shape of the electrocardiogram signal.<br \/>\nUsing those tokens as the input of a neural network, an average accuracy of 98% can be achieved using the MIT-BIH database. However, because training a neural network with unbalanced datasets introduces a bias, a novel classification scheme based on antidictionaries is introduced. It consists of patterns created from negative information about healthy heartbeats, allowing the detection of arrhythmic heartbeats by training AI-based systems without introducing a bias. Antidictionary-based classification achieves a similar average accuracy of 98%, showing its ability to classify arrhythmia on par with state-of-the-art neural networks.<br \/>\nThe system implemented on FPGA necessitates 99% less memory to store the classifier, 1,024 logic comparisons instead of 512 multiplication operations, and 99% less accumulation com pared to the state of the art.<br \/>\nThis results in 25.1% less LUT and 34.5% less register used on the FPGA compared to the state of the art and does not require the implementation of any RAM. For those benefits, the overall classification accuracy was reduced by 1.03 percentage points compared to the state of the art with a classification accuracy of 97.97%.<\/p>\n<\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":20,"featured_media":71083,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,8,319,87,65,84],"tags":[],"class_list":["post-71080","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-a-la-une","category-actualites","category-actualites2022","category-agenda-en","category-agenda","category-agenda-en-en"],"_links":{"self":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/71080","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/comments?post=71080"}],"version-history":[{"count":0,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/71080\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media\/71083"}],"wp:attachment":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media?parent=71080"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/categories?post=71080"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/tags?post=71080"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}