{"id":2696,"date":"2017-06-15T13:27:16","date_gmt":"2017-06-15T18:27:16","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=2696"},"modified":"2026-02-18T11:28:58","modified_gmt":"2026-02-18T16:28:58","slug":"jiao2017multiple-2","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2017\/06\/15\/jiao2017multiple-2\/","title":{"rendered":"Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DLFUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a \u201cheartbeat concept\u201d that represents an individual\u2019s personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept found by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms. <\/p>\n<h2>Links:<\/h2>\n<p><a href=\"http:\/\/ieeexplore.ieee.org\/document\/8307229\/\"><img decoding=\"async\" border=\"2\" alt=\"\u201cIeee\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg\" height=\"50\"><\/a><a href=\"https:\/\/arxiv.org\/abs\/1706.03373\"><img decoding=\"async\" border=\"2\" alt=\"\u201cArXiv\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/Jiao2017Multiple.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a><a href=\"https:\/\/github.com\/GatorSense\/FUMI\"><img decoding=\"async\" border=\"2\" alt=\"\u201cCode\u201d\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/GitHub-Mark-e1482256611783.png\" height=\"50\"><\/a> <\/p>\n<h2>Citation:<\/h2>\n<pre><code>C. Jiao, B. Su, P. Lyons, A. Zare, K. C. Ho and M. Skubic, \"Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms,\"<em>IEEE Trans. Biomed. Eng.<\/em>, To Appear.<\/code><\/pre>\n<pre><code>@Article{Jiao2017Multiple,\nTitle = {Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms},\nAuthor = {Jiao, Changzhe and Su, Bo-Yu and Lyons, Princess and Zare, Alina and Ho, K. C. and Skubic, Marjorie},\nJournal = {<em>IEEE Trans. Biomed. Eng.<\/em>},\nYear = {2018},\nvolume = {65},\nnumber = {11},\npages = {2634-2648},\nmonth = {Nov.},\ndoi = {10.1109\/TBME.2018.2812602},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DLFUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a \u201cheartbeat concept\u201d that represents an individual\u2019s personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":3508,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[5,19],"tags":[53,129,235,349,487,733],"class_list":["post-2696","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-featured","category-journal_paper","tag-uncertain-imprecise-labels","tag-biomedical","tag-dictionary-learning","tag-heartbeat","tag-multiple-instance","tag-target-detection"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/2696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=2696"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/2696\/revisions"}],"predecessor-version":[{"id":14947,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/2696\/revisions\/14947"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media\/3508"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=2696"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=2696"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=2696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}