{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:25:41Z","timestamp":1775838341448,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T00:00:00Z","timestamp":1640304000000},"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>Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss. The autoencoder has an embedded attention module at the bottleneck to learn the salient activations of the encoded distribution. Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data analysis. The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy heartbeats from patients with severe congestive heart failure.<\/jats:p>","DOI":"10.3390\/s22010123","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9853-9554","authenticated-orcid":false,"given":"Ariyo","family":"Oluwasanmi","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9649-7757","authenticated-orcid":false,"given":"Muhammad Umar","family":"Aftab","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4088-6622","authenticated-orcid":false,"given":"Edward","family":"Baagyere","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6745-6377","authenticated-orcid":false,"given":"Zhiguang","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3320-2261","authenticated-orcid":false,"given":"Muhammad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3860-4948","authenticated-orcid":false,"given":"Manuel","family":"Mazzara","sequence":"additional","affiliation":[{"name":"Institute of Software Development and Engineering, Innopolis University, Innopolis 420500, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lei, Y., Yang, B., Xinwei, J., Jia, F., Li, N., and Nandi, A. 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