{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:20:32Z","timestamp":1783938032900,"version":"3.55.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> We previously developed a non-invasive approach to localize the site of early left ventricular activation origin in real time using 12-lead ECG, and to project the predicted site onto a generic LV endocardial surface using the smallest angle between two vectors algorithm (SA).<\/jats:p><jats:p><jats:bold>Objectives:<\/jats:bold> To improve the localization accuracy of the non-invasive approach by utilizing the K-nearest neighbors algorithm (KNN) to reduce projection errors.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Two datasets were used. Dataset #1 had 1012 LV endocardial pacing sites with known coordinates on the generic LV surface and corresponding ECGs, while dataset #2 included 25 clinically-identified VT exit sites and corresponding ECGs. The non-invasive approach used \u201cpopulation\u201d regression coefficients to predict the target coordinates of a pacing site or VT exit site from the initial 120-m QRS integrals of the pacing site\/VT ECG. The predicted site coordinates were then projected onto the generic LV surface using either the KNN or SA projection algorithm.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The non-invasive approach using the KNN had a significantly lower mean localization error than the SA in both dataset #1 (9.4 vs. 12.5\u00a0mm, <jats:italic>p<\/jats:italic> &amp;lt; 0.05) and dataset #2 (7.2 vs. 9.5\u00a0mm, <jats:italic>p<\/jats:italic> &amp;lt; 0.05). The bootstrap method with 1,000 trials confirmed that using KNN had significantly higher predictive accuracy than using the SA in the bootstrap assessment with the left-out sample (<jats:italic>p<\/jats:italic> &amp;lt; 0.05).<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> The KNN significantly reduces the projection error and improves the localization accuracy of the non-invasive approach, which shows promise as a tool to identify the site of origin of ventricular arrhythmia in non-invasive clinical modalities.<\/jats:p>","DOI":"10.3389\/fphys.2023.1183280","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T05:24:38Z","timestamp":1687757078000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving localization accuracy for non-invasive automated early left ventricular origin localization approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Shijie","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avery","family":"Seagren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noah","family":"Emmert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James W.","family":"Warren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"MacInnis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"AbdelWahab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John L.","family":"Sapp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1109\/TMI.2018.2880092","article-title":"Learning domain shift in simulated and clinical data: Localizing the origin of ventricular activation from 12-lead electrocardiograms","volume":"38","author":"Alawad","year":"2019","journal-title":"IEEE Trans. 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