{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:44:29Z","timestamp":1772729069746,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"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>Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model\u2019s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network\u2019s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method\u2019s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.<\/jats:p>","DOI":"10.3390\/computers10060082","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T11:20:26Z","timestamp":1623928826000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-4551","authenticated-orcid":false,"given":"Ahmad O.","family":"Aseeri","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.cjca.2020.02.096","article-title":"Usefulness of machine learning-based detection and classification of cardiac arrhythmias with 12-lead electrocardiograms","volume":"37","author":"Chang","year":"2021","journal-title":"Can. J. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MC.2016.339","article-title":"Machine learning in cardiac health monitoring and decision support","volume":"49","author":"Hijazi","year":"2016","journal-title":"Computer"},{"key":"ref_3","first-page":"1","article-title":"Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram","volume":"11","author":"Besomi","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1161\/CIRCULATIONAHA.120.050231","article-title":"Artificial Intelligence\u2013Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device","volume":"143","author":"Giudicessi","year":"2021","journal-title":"Circulation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1093\/europace\/euz349","article-title":"Artificial Intelligence Capable of Detecting Left Ventricular Hypertrophy: Pushing the Limits of the Electrocardiogram?","volume":"22","author":"Kashou","year":"2020","journal-title":"Europace"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kuncheva, L. (2000). Fuzzy Classifier Design, Springer Science & Business Media.","DOI":"10.1007\/978-3-7908-1850-5"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, Z., Bai, H., Liu, W., and Wang, L. (2020). Seizure classification from EEG signals using an online selective transfer TSK fuzzy classifier with joint distribution adaption and manifold regularization. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.00496"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Postorino, M.N., and Versaci, M. (2014). A geometric fuzzy-based approach for airport clustering. Adv. Fuzzy Syst., 2014.","DOI":"10.1155\/2014\/201243"},{"key":"ref_9","unstructured":"Neal, R.M. (2012). Bayesian Learning for Neural Networks, Springer Science & Business Media."},{"key":"ref_10","unstructured":"Jospin, L.V., Buntine, W., Boussaid, F., Laga, H., and Bennamoun, M. (2020). Hands-on Bayesian Neural Networks\u2014A Tutorial for Deep Learning Users. arXiv."},{"key":"ref_11","unstructured":"Gal, Y., and Ghahramani, Z. (2016, January 20\u201322). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Proceedings of the International Conference on Machine Learning, PMLR, New York City, NY, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hein, M., Andriushchenko, M., and Bitterwolf, J. (2019, January 19\u201320). Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00013"},{"key":"ref_13","unstructured":"Moon, J., Kim, J., Shin, Y., and Hwang, S. (2020, January 13\u201318). Confidence-aware learning for deep neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gacek, A., and Pedrycz, W. (2012). An Introduction to ECG Signal Processing and Analysis. ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence, Springer.","DOI":"10.1007\/978-0-85729-868-3"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S1386-5056(98)00138-5","article-title":"ECG pattern recognition and classification using non-linear transformations and neural networks: A review","volume":"52","author":"Maglaveras","year":"1998","journal-title":"Int. J. Med. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3238","DOI":"10.1016\/j.measurement.2013.05.021","article-title":"ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier","volume":"46","author":"Rai","year":"2013","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1161\/CIRCULATIONAHA.108.770917","article-title":"Fragmented QRS as a marker of conduction abnormality and a predictor of prognosis of Brugada syndrome","volume":"118","author":"Morita","year":"2008","journal-title":"Circulation"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2018.2844195","article-title":"QRS complex detection and measurement algorithms for multichannel ECGs in cardiac resynchronization therapy patients","volume":"6","author":"Curtin","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/10.362922","article-title":"Detection of ECG characteristic points using wavelet transforms","volume":"42","author":"Li","year":"1995","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","first-page":"2067","article-title":"Gated Feedback Recurrent Neural Networks","volume":"Volume 37","author":"Bach","year":"2015","journal-title":"Proceedings of the 32nd International Conference on Machine Learning"},{"key":"ref_21","unstructured":"Comminiello, D., and Principe, J.C. (2018). Chapter 12-Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models. Adaptive Learning Methods for Nonlinear System Modeling, Butterworth-Heinemann."},{"key":"ref_22","unstructured":"Olah, C. (2021, June 17). Understanding LSTM Networks. Available online: https:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/."},{"key":"ref_23","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merrinboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_27","unstructured":"Graves, A. (2011, January 12\u201315). Practical variational inference for neural networks. Proceedings of the Advances in Neural Information Processing Systems, Granada, Spain."},{"key":"ref_28","unstructured":"Kendall, A., and Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision?. arXiv."},{"key":"ref_29","unstructured":"Welling, M., and Teh, Y.W. (July, January 28). Bayesian learning via stochastic gradient Langevin dynamics. Proceedings of the 28th international conference on machine learning (ICML-11), Citeseer, Bellevue, WA, USA."},{"key":"ref_30","unstructured":"Hern\u00e1ndez-Lobato, J.M., and Adams, R. (2015, January 7\u20139). Probabilistic backpropagation for scalable learning of bayesian neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_31","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015, January 7\u20139). Weight uncertainty in neural network. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH arrhythmia database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/S0735-1097(86)80478-8","article-title":"Survival of patients with severe congestive heart failure treated with oral milrinone","volume":"7","author":"Baim","year":"1986","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_35","unstructured":"(2021, June 17). Research Resource for Complex Physiologic Signals. Available online: https:\/\/physionet.org\/."},{"key":"ref_36","unstructured":"Association for the Advancement of Medical Instrumentation (1998). Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms, Association for the Advancement of Medical Instrumentation."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1046\/j.1540-8167.2004.03604.x","article-title":"Relationship between QRS duration and left ventricular dyssynchrony in patients with end-stage heart failure","volume":"15","author":"Bleeker","year":"2004","journal-title":"J. Cardiovasc. Electrophysiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1001\/archinternmed.2011.247","article-title":"Impact of QRS duration on clinical event reduction with cardiac resynchronization therapy: Meta-analysis of randomized controlled trials","volume":"171","author":"Sipahi","year":"2011","journal-title":"Arch. Intern. Med."},{"key":"ref_39","unstructured":"Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2016). Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-17876-z","article-title":"Leveraging uncertainty information from deep neural networks for disease detection","volume":"7","author":"Leibig","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_41","unstructured":"Gal, Y., and Ghahramani, Z. (2015). Dropout as a Bayesian approximation: Appendix. arXiv."},{"key":"ref_42","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2021, June 17). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_43","unstructured":"Team, K. (2021, June 17). Simple. Flexible. Powerful. Available online: https:\/\/www.myob.com\/nz\/about\/news\/2020\/simple\u2013flexible\u2013powerful\u2014the-new-myob-essentials."},{"key":"ref_44","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Aseeri, A. (2020). Noise-Resilient Neural Network-Based Adversarial Attack Modeling for XOR Physical Unclonable Functions. J. Cyber Secur. Mobil., 331\u2013354.","DOI":"10.13052\/jcsm2245-1439.926"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_47","unstructured":"Henne, M., Schwaiger, A., Roscher, K., and Weiss, G. (2021, June 17). Benchmarking Uncertainty Estimation Methods for Deep Learning With Safety-Related Metrics. Available online: http:\/\/ceur-ws.org\/Vol-2560\/paper35.pdf."},{"key":"ref_48","unstructured":"Mukhoti, J., and Gal, Y. (2018). Evaluating bayesian deep learning methods for semantic segmentation. arXiv."},{"key":"ref_49","first-page":"1","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","unstructured":"Ye, C., Kumar, B.V., and Coimbra, M.T. (2012, January 11\u201315). Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compbiomed.2013.11.019","article-title":"Heartbeat classification using disease-specific feature selection","volume":"46","author":"Zhang","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_52","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"He, Z., Zhang, X., Cao, Y., Liu, Z., Zhang, B., and Wang, X. (2018). LiteNet: Lightweight neural network for detecting arrhythmias at resource-constrained mobile devices. Sensors, 18.","DOI":"10.3390\/s18041229"},{"key":"ref_55","unstructured":"Jun, T.J., Nguyen, H.M., Kang, D., Kim, D., Kim, D., and Kim, Y.H. (2018). ECG arrhythmia classification using a 2-D convolutional neural network. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"47103","DOI":"10.1109\/ACCESS.2020.2979256","article-title":"Arrhythmia recognition and classification using combined parametric and visual pattern features of ECG morphology","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Carvalho, C.S. (2020). A deep-learning classifier for cardiac arrhythmias. arXiv.","DOI":"10.1109\/BIBE50027.2020.9509791"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/6\/82\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:17:43Z","timestamp":1760163463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/6\/82"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,17]]},"references-count":57,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["computers10060082"],"URL":"https:\/\/doi.org\/10.3390\/computers10060082","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,17]]}}}