{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T01:42:39Z","timestamp":1769218959211,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Background and objective: Arrhythmia is a widely seen cardiologic ailment worldwide, and is diagnosed using electrocardiogram (ECG) signals. The ECG signals can be translated manually by human experts, but can also be scheduled to be carried out automatically by some agents. To easily diagnose arrhythmia, an intelligent assistant can be used. Machine learning-based automatic arrhythmia detection models have been proposed to create an intelligent assistant. Materials and Methods: In this work, we have used an ECG dataset. This dataset contains 1000 ECG signals with 17 categories. A new hand-modeled learning network is developed on this dataset, and this model uses a 3D shape (prismatoid) to create textural features. Moreover, a tunable Q wavelet transform with low oscillatory parameters and a statistical feature extractor has been applied to extract features at both low and high levels. The suggested prismatoid pattern and statistical feature extractor create features from 53 sub-bands. A neighborhood component analysis has been used to choose the most discriminative features. Two classifiers, k nearest neighbor (kNN) and support vector machine (SVM), were used to classify the selected top features with 10-fold cross-validation. Results: The calculated best accuracy rate of the proposed model is equal to 97.30% using the SVM classifier. Conclusion: The computed results clearly indicate the success of the proposed prismatoid pattern-based model.<\/jats:p>","DOI":"10.3390\/sym13101914","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T21:45:32Z","timestamp":1633988732000},"page":"1914","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture"],"prefix":"10.3390","volume":"13","author":[{"given":"Mehmet Ali","family":"Kobat","sequence":"first","affiliation":[{"name":"Department of Cardiology, Firat University Hospital, Firat University, 23119 Elazig, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ozkan","family":"Karaca","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Gaziantep Dr. Ersin Arslan Education Research Hospital, 27010 Gaziantep, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5117-8333","authenticated-orcid":false,"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[{"name":"School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia"},{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering, College of Technology, Firat University, 23119 Elazig, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.molmed.2015.10.002","article-title":"New approaches to biological pacemakers: Links to sinoatrial node development","volume":"21","author":"Vedantham","year":"2015","journal-title":"Trends Mol. 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