{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:50:29Z","timestamp":1776919829338,"version":"3.51.2"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MCTES","award":["UIDB\/EEA\/50008\/2020"],"award-info":[{"award-number":["UIDB\/EEA\/50008\/2020"]}]},{"name":"FEDER\u2014PT2020 partnership agreement","award":["UIDB\/EEA\/50008\/2020"],"award-info":[{"award-number":["UIDB\/EEA\/50008\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals\u2019 nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal\u2019s structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070\u00b10.0028 and an average MAE of 0.0522\u00b10.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model\u2019s accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.<\/jats:p>","DOI":"10.3390\/bioengineering11010089","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T10:39:47Z","timestamp":1705487987000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["ECG Forecasting System Based on Long Short-Term Memory"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1924-1464","authenticated-orcid":false,"given":"Henriques","family":"Zacarias","sequence":"first","affiliation":[{"name":"Faculdade de Ci\u00eancias de Sa\u00fade, Universidade da Beira Interior, 6201-001 Covilha, Portugal"},{"name":"Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal"},{"name":"Instituto Polit\u00e9cnico da Hu\u00edla, Universidade Mandume Ya Ndemufayo, Lubango 1049-001, Angola"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6472-8784","authenticated-orcid":false,"given":"Jo\u00e3o Alexandre L\u00f4bo","family":"Marques","sequence":"additional","affiliation":[{"name":"Laboratory of Applied Neurosciences, University of Saint Joseph, Macau 999078, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6874-3263","authenticated-orcid":false,"given":"Virginie","family":"Felizardo","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal"},{"name":"Departamento de Inform\u00e1tica, Universidade da Beira Interior, 6201-001 Covilha, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0034-5506","authenticated-orcid":false,"given":"Mehran","family":"Pourvahab","sequence":"additional","affiliation":[{"name":"Departamento de Inform\u00e1tica, Universidade da Beira Interior, 6201-001 Covilha, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3195-3168","authenticated-orcid":false,"given":"Nuno M.","family":"Garcia","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal"},{"name":"Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"ref_1","first-page":"3645","article-title":"Big data analytics in healthcare: A survey","volume":"10","author":"Ebenezer","year":"2015","journal-title":"J. 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