{"id":1521,"date":"2022-12-20T12:40:30","date_gmt":"2022-12-20T12:40:30","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1521"},"modified":"2022-12-20T12:40:51","modified_gmt":"2022-12-20T12:40:51","slug":"vol20-no2-art3","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art3\/","title":{"rendered":"Intelligent Detection of Arrhythmia Episodes in Dialysis Patients"},"content":{"rendered":"<p>Sergio Pinto Gomes Junior <a href=\"https:\/\/orcid.org\/0000-0003-1166-4286\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Jo\u00e3o Baptista de Oliveira e Souza Filho <a href=\"https:\/\/orcid.org\/0000-0001-6005-8480\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Felipe da Rocha Henriques <a href=\"https:\/\/orcid.org\/0000-0002-7221-7466\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>&#038; Michel Pompeu Tcheou <a href=\"https:\/\/orcid.org\/0000-0003-2068-2865\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> This work discusses the design of an automatic detector of arrhythmia episodes in patients submitted to dialysis. The system aims to operate on portable devices in real-time, allowing a faster response of healthcare workers to possible intercurrence episodes. The detection is based on processing short windows of samples extracted from the electrocardiogram signal around the R-wave peak in raw format. A comprehensive study evaluating several classification techniques and class-imbalance strategies is conducted based on the MIT-BIH Arrhythmia Database. Besides, a new procedure for tuning the sample window length based on an experimental feature importance cumulative distribution is proposed. Results show that a Random Forest classifier, trained with minority class oversampling, is cost-effective regarding complexity and computational cost, achieving an accuracy of 98.7% for windows sizes as small as 105 samples.<\/p>\n<p><strong>Keywords:<\/strong> Dialysis, arrhythmia, beat classifier, clinical decision support.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol20-no2-art3\">10.21528\/lnlm-vol20-no2-art3<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2022\/12\/vol20-no2-art3.pdf\">vol20-no2-art3.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2022\/12\/vol20-no2-art3.bib\">vol20-no2-art3.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sergio Pinto Gomes Junior , Jo\u00e3o Baptista de Oliveira e Souza Filho , Felipe da Rocha Henriques &#038; Michel Pompeu Tcheou Abstract: This work discusses the design of an automatic detector of arrhythmia episodes in patients submitted to dialysis. The <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art3\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1512,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1521","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Intelligent Detection of Arrhythmia Episodes in Dialysis Patients - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Intelligent Detection of Arrhythmia Episodes in Dialysis Patients - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Sergio Pinto Gomes Junior , Jo\u00e3o Baptista de Oliveira e Souza Filho , Felipe da Rocha Henriques &#038; Michel Pompeu Tcheou Abstract: This work discusses the design of an automatic detector of arrhythmia episodes in patients submitted to dialysis. 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