{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:33:55Z","timestamp":1774294435176,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"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>One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient\u2019s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error &lt; 13 \u03bcV and CC &gt; 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.<\/jats:p>","DOI":"10.3390\/s21165542","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T22:51:00Z","timestamp":1629327060000},"page":"5542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0493-3695","authenticated-orcid":false,"given":"Alejandro","family":"Grande-Fidalgo","sequence":"first","affiliation":[{"name":"Analog Devices, Inc., 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3418-7099","authenticated-orcid":false,"given":"Javier","family":"Calpe","sequence":"additional","affiliation":[{"name":"Analog Devices, Inc., 46980 Paterna, Spain"}]},{"given":"M\u00f3nica","family":"Red\u00f3n","sequence":"additional","affiliation":[{"name":"Analog Devices, Inc., 46980 Paterna, Spain"}]},{"given":"Carlos","family":"Mill\u00e1n-Navarro","sequence":"additional","affiliation":[{"name":"Analog Devices, Inc., 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9148-8405","authenticated-orcid":false,"given":"Emilio","family":"Soria-Olivas","sequence":"additional","affiliation":[{"name":"IDAL, Intelligent Data Analysis Laboratory, Escuela T\u00e9cnica Superior de Ingenier\u00eda, Universidad de Valencia, 46100 Burjassot, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"ref_1","unstructured":"Paine, R. 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