{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T07:24:05Z","timestamp":1787124245287,"version":"build-2736575974"},"reference-count":19,"publisher":"IOP Publishing","issue":"8","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Open Access Publishing Fund of Leipzig University"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Electrocardiogramms (ECG) interpretation is increasingly supported by algorithms trained on large-scale datasets. Here we present a first manually annotated open-source ECG-database of children and adults with congenital heart defect (CHD) with arrhythmia annotations.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Signal data (ECG and intracardiac electrograms) were extracted from children and patients with CHD undergoing electrophysiological studies (EPS). The dataset is provided in the wave form database format. Annotations were manually annotated using the LightWAVE\u00ae software.\n                    <jats:italic>Main results<\/jats:italic>\n                    . The dataset comprises 39 ECGs from 39 patients (duration ranging from 00:01:17 h to 02:30:31 h). A total of 113 924 beats were annotated. A large part of beats (33%) are normal sinus rhythm. However, the database also includes sinus rhythm beats with preexcitation (12%) or complete right bundle branch block (11%). Supraventricular tachycardias (23%) are further categorized into atrioventricular reentrant tachycardias (AVRT; 15.5%) and atrioventricular nodal reentrant tachycardias (AVNRT; 7.5%). Also, ventricular tachycardias (1.7%) and very rare conditions as aberrant conducted AVRT or AVNRT or AVNRT with 2nd degree heart block are represented. Additionally paced beats are included (12%), thereof 7% atrial paced beats and 5% ventricular paced beats. Premature beats (premature ventricular or atrial beats) make up 7% of the dataset.\n                    <jats:italic>Significance<\/jats:italic>\n                    . This open-source database (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/physionet.org\/content\/leipzig-heart-center-ecg\">https:\/\/physionet.org\/content\/leipzig-heart-center-ecg<\/jats:ext-link>\n                    ) containing many pathologies in children and patients with CHD may provide a foundation for ECG algorithms aimed at detecting ECG pathologies in this special patient population.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae9033","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T22:54:57Z","timestamp":1784933697000},"page":"085024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Leipzig heart center ECG-database: arrhythmias in children and patients with congenital heart disease"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7715-2799","authenticated-orcid":true,"given":"Sophia","family":"Klehs","sequence":"first","affiliation":[{"name":"University of Leipzig\u2014Heart Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0859-7583","authenticated-orcid":true,"given":"Daniel","family":"Franke","sequence":"additional","affiliation":[{"name":"University of Applied Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5778-7109","authenticated-orcid":true,"given":"Bayhas","family":"Alhamad","sequence":"additional","affiliation":[{"name":"University of Leipzig\u2014Heart Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman","family":"Gebauer","sequence":"additional","affiliation":[{"name":"University of Leipzig\u2014Heart Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linus","family":"Teich","sequence":"additional","affiliation":[{"name":"University of Applied Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2773-689X","authenticated-orcid":true,"given":"Ingo","family":"D\u00e4hnert","sequence":"additional","affiliation":[{"name":"University of Leipzig\u2014Heart Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1736-9296","authenticated-orcid":true,"given":"Tobias","family":"Teich","sequence":"additional","affiliation":[{"name":"University of Applied Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9676-8994","authenticated-orcid":true,"given":"Christian","family":"Paech","sequence":"additional","affiliation":[{"name":"University of Leipzig\u2014Heart Center"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"pmeaae9033bib1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","type":"journal-article","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction","volume":"394","author":"Attia","year":"2019","journal-title":"Lancet"},{"key":"pmeaae9033bib2","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2024.101712","type":"journal-article","article-title":"BioSPPy: a python toolbox for physiological signal processing","volume":"26","author":"Bota","year":"2024","journal-title":"Softwarex"},{"key":"pmeaae9033bib3","doi-asserted-by":"publisher","DOI":"10.1177\/11779322221149600","type":"journal-article","article-title":"A literature review: ECG-based models for arrhythmia diagnosis using artificial intelligence techniques","volume":"17","author":"Boulif","year":"2023","journal-title":"Bioinform. 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