{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T05:59:14Z","timestamp":1776751154135,"version":"3.51.2"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MINISTERIO DE ECONOMIA Y COMPETITIVIDAD","award":["IPT-2012-1126-300000"],"award-info":[{"award-number":["IPT-2012-1126-300000"]}]},{"name":"MINISTERIO DE ECONOMIA Y COMPETITIVIDAD","award":["BEYOND AEI\/10.13039\/5011000110033-PID2019-106623RB"],"award-info":[{"award-number":["BEYOND AEI\/10.13039\/5011000110033-PID2019-106623RB"]}]},{"name":"MINISTERIO DE ECONOMIA Y COMPETITIVIDAD","award":["meHeart AEI\/10.13039\/5011000110033\u00f1 PID2019-104356RB"],"award-info":[{"award-number":["meHeart AEI\/10.13039\/5011000110033\u00f1 PID2019-104356RB"]}]},{"name":"MINISTERIO DE ECONOMIA Y COMPETITIVIDAD","award":["LATENTIA AEI\/10.13039\/5011000110033-PID2022-140786NB-C31"],"award-info":[{"award-number":["LATENTIA AEI\/10.13039\/5011000110033-PID2022-140786NB-C31"]}]},{"name":"MINISTERIO DE ECONOMIA Y COMPETITIVIDAD","award":["HESTIA 2022-REGING-92049"],"award-info":[{"award-number":["HESTIA 2022-REGING-92049"]}]},{"name":"MINISTERIO DE ECONOMIA Y COMPETITIVIDAD","award":["MI-OBESIDAD 2022-REGING-95982"],"award-info":[{"award-number":["MI-OBESIDAD 2022-REGING-95982"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in electrode-recorded biopotentials. The present work aims to determine whether low-dimensional latent spaces could exhibit discriminative features for different mechanisms or conditions during VF episodes. For this purpose, manifold learning using autoencoder neural networks was analyzed based on surface ECG recordings. The recordings covered the onset of the VF episode as well as the next 6 min, and comprised an experimental database based on an animal model with five situations, including control, drug intervention (amiodarone, diltiazem, and flecainide), and autonomic nervous system blockade. The results show that latent spaces from unsupervised and supervised learning schemes yielded moderate though quite noticeable separability among the different types of VF according to their type or intervention. In particular, unsupervised schemes reached a multi-class classification accuracy of 66%, while supervised schemes improved the separability of the generated latent spaces, providing a classification accuracy of up to 74%. Thus, we conclude that manifold learning schemes can provide a valuable tool for studying different types of VF while working in low-dimensional latent spaces, as the machine-learning generated features exhibit separability among different VF types. This study confirms that latent variables are better VF descriptors than conventional time or domain features, making this technique useful in current VF research on elucidation of the underlying VF mechanisms.<\/jats:p>","DOI":"10.3390\/s23052527","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:10:46Z","timestamp":1677463846000},"page":"2527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1340-3234","authenticated-orcid":false,"given":"Carlos Pa\u00fal","family":"Bernal O\u00f1ate","sequence":"first","affiliation":[{"name":"Departamento de El\u00e9ctrica, Electr\u00f3nica y Telecomunicaciones, Universidad de las Fuerzas Armadas\u2014ESPE, Sangolqui 171103, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6916-6082","authenticated-orcid":false,"given":"Francisco-Manuel","family":"Melgarejo Meseguer","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, Telematics and Computing Systems, Universidad Rey Juan Carlos, 28943 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7519-3167","authenticated-orcid":false,"given":"Enrique V.","family":"Carrera","sequence":"additional","affiliation":[{"name":"Departamento de El\u00e9ctrica, Electr\u00f3nica y Telecomunicaciones, Universidad de las Fuerzas Armadas\u2014ESPE, Sangolqui 171103, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6560-9429","authenticated-orcid":false,"given":"Juan Jos\u00e9","family":"S\u00e1nchez Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Arrhythmia Unit, University Hospital Virgen de la Arrixaca, 30120 El Palmar, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1928-865X","authenticated-orcid":false,"given":"Arcadi","family":"Garc\u00eda Alberola","sequence":"additional","affiliation":[{"name":"Arrhythmia Unit, University Hospital Virgen de la Arrixaca, 30120 El Palmar, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0426-8912","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Rojo \u00c1lvarez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, Telematics and Computing Systems, Universidad Rey Juan Carlos, 28943 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1002\/emmm.201000066","article-title":"In search of the sources of cardiac fibrillation","volume":"2","year":"2010","journal-title":"EMBO Mol. 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