{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:24:33Z","timestamp":1790832273266,"version":"4.1.0"},"reference-count":43,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","award":["FS\/20\/20\/34626"],"award-info":[{"award-number":["FS\/20\/20\/34626"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["RP-PG0217-20007"],"award-info":[{"award-number":["RP-PG0217-20007"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["SPCR-2014-10043"],"award-info":[{"award-number":["SPCR-2014-10043"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (\n                      <jats:italic>F<\/jats:italic>\n                      <jats:sub>1<\/jats:sub>\n                      score \u22650.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved\n                      <jats:italic>F<\/jats:italic>\n                      <jats:sub>1<\/jats:sub>\n                      of 0.78-0.84). The presence of AF had little impact on performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pdig.0000538","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T13:21:42Z","timestamp":1723555302000},"page":"e0000538","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms"],"prefix":"10.1371","volume":"3","author":[{"given":"Florian","family":"Kristof","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Kapsecker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leon","family":"Nissen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Brimicombe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin R.","family":"Cowie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Dymond","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan M.","family":"Jonas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah Clair","family":"Lind\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory Y. H.","family":"Lip","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6188-9363","authenticated-orcid":true,"given":"Kate","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Mant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3836-8655","authenticated-orcid":true,"given":"Peter H.","family":"Charlton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"on behalf of the SAFER Investigators","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"issue":"1","key":"pdig.0000538.ref001","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/europace\/euaa139","article-title":"Mobile health applications for the detection of atrial fibrillation: a systematic review","volume":"23","author":"CR Lopez Perales","year":"2021","journal-title":"EP Europace"},{"issue":"11","key":"pdig.0000538.ref002","doi-asserted-by":"crossref","first-page":"e18907","DOI":"10.2196\/18907","article-title":"Wearable health devices in health care: narrative systematic review","volume":"8","author":"L Lu","year":"2020","journal-title":"JMIR mHealth and uHealth"},{"key":"pdig.0000538.ref003","doi-asserted-by":"crossref","first-page":"120712","DOI":"10.1016\/j.techfore.2021.120712","article-title":"Opportunities and challenges for contactless healthcare services in the post-COVID-19 Era","volume":"167","author":"SM Lee","year":"2021","journal-title":"Technol Forecast Soc Change"},{"issue":"7","key":"pdig.0000538.ref004","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TBME.2016.2549060","article-title":"QRS detection algorithm for telehealth electrocardiogram recordings","volume":"63","author":"H Khamis","year":"2016","journal-title":"IEEE Trans Biomed Eng"},{"key":"pdig.0000538.ref005","first-page":"e9050812","article-title":"Performance analysis of ten common QRS detectors on different ECG application cases","volume":"2018","author":"F Liu","year":"2018","journal-title":"J Healthcare Eng"},{"key":"pdig.0000538.ref006","first-page":"357","article-title":"QRS detectors performance comparison in public databases","author":"M Llamedo","year":"2014","journal-title":"Computing in Cardiology"},{"issue":"9","key":"pdig.0000538.ref007","doi-asserted-by":"crossref","first-page":"e73557","DOI":"10.1371\/journal.pone.0073557","article-title":"Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases","volume":"8","author":"M Elgendi","year":"2013","journal-title":"PLoS ONE"},{"issue":"4","key":"pdig.0000538.ref008","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"D Makowski","year":"2021","journal-title":"Behav Res"},{"key":"pdig.0000538.ref009","unstructured":"Porr B, Howell L. py-ecg-detectors: Seven ECG heartbeat detection algorithms and heartrate variability analysis. 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