{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:07:45Z","timestamp":1780708065029,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and providing information about the patient\u2019s cardiac health to the specialist. Current advancements in deep-learning-based multivariate time series data analysis, such as ECG data classification include LSTM, Bi-LSTM, CNN, with Bi-LSTM, and other sequential networks. However, these networks often struggle to accurately determine the long-range dependencies among data instances, which can result in problems such as vanishing or exploding gradients for longer data sequences. To address these shortcomings of sequential models, a hybrid arrhythmia classification system using recurrence along with a self-attention mechanism is developed. This system utilizes convolutional layers as a part of representation learning, designed to capture the salient features of raw ECG data. Then, the latent embedded layer is fed to a self-attention-assisted transformer encoder model. Because the ECG data are highly influenced by absolute order, position, and proximity of time steps due to interdependent relationships among immediate neighbors, a component of recurrence using Bi-LSTM is added to the encoder model to address this characteristic of the data. The model performance indices such as classification accuracy and F1-score were found to be 99.2%. This indicates that the combination of recurrence along with self-attention-assisted architecture produces improved classification of arrhythmia from raw ECG signal when compared with the state-of-the-art models.<\/jats:p>","DOI":"10.3390\/computers12030068","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T06:56:48Z","timestamp":1679381808000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Temporal Transformer-Based Fusion Framework for Morphological Arrhythmia Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Nafisa","family":"Anjum","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0031-9284","authenticated-orcid":false,"given":"Khaleda Akhter","family":"Sathi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Azad","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-7509","authenticated-orcid":false,"given":"M. Ali Akber","family":"Dewan","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"ref_1","unstructured":"(2022, October 23). World Health Organization. Available online: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases."},{"key":"ref_2","unstructured":"Mayo Clinic (2022, October 23). Diseases and Conditions. 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