{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:58:17Z","timestamp":1779263897158,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart\u2019s surface using the potentials recorded at the body\u2019s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs\u2019 ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.<\/jats:p>","DOI":"10.3390\/s22062331","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"2331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks"],"prefix":"10.3390","volume":"22","author":[{"given":"Ke-Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of BioMedical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Bear","sequence":"additional","affiliation":[{"name":"Electrophysiology and Heart Modelling Institute (IHU-LIRYC), Fondation Bordeaux Universit\u00e9, 33000 Bordeaux, France"},{"name":"Centre de Recherche Cardio-Thoracique de Bordeaux, INSERM U1045, Universit\u00e9 de Bordeaux, 33600 Pessac, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1894-1189","authenticated-orcid":false,"given":"Che-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of BioMedical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1161\/01.CIR.96.3.1012","article-title":"Noninvasive electrocardiographic imaging: Reconstruction of epicardial potentials, electrograms, and isochrones and localization of single and multiple electrocardiac events","volume":"96","author":"Oster","year":"1997","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.ccep.2019.05.004","article-title":"Noninvasive Mapping and Electrocardiographic Imaging in Atrial and Ventricular Arrhythmias (CardioInsight)","volume":"11","author":"Cheniti","year":"2019","journal-title":"Card. 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