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Project of Chengdu","award":["XZ202501ZY0094"],"award-info":[{"award-number":["XZ202501ZY0094"]}]},{"name":"Key Research and Development Support Program Project of Chengdu","award":["2022YFB3103103"],"award-info":[{"award-number":["2022YFB3103103"]}]},{"name":"Key Research and Development Support Program Project of Chengdu","award":["2023-XT00-00002-GX"],"award-info":[{"award-number":["2023-XT00-00002-GX"]}]},{"name":"Key Research and Development Support Program Project of Chengdu","award":["2024-YF05-01227-SN"],"award-info":[{"award-number":["2024-YF05-01227-SN"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Electrocardiogram (ECG) interpretation using deep learning models holds immense potential for improving cardiac diagnosis. However, existing models often suffer from overconfident predictions and lack the capability to directly quantify uncertainty, leading to unreliable clinical guidance. To address this challenge, we propose a model for uncertainty-aware ECG interpretation. The model employs a deep convolutional architecture with max-pooling residual modules to capture both local and global spatiotemporal features from raw ECG signals. The architectural design respects the symmetry inherent in ECG waveforms\u2014such as periodicity and morphological consistency across cardiac cycles\u2014enabling the network to extract clinically relevant features more effectively. Then, unlike conventional models that rely on softmax-based probability outputs, our approach parameterizes class distributions using the Dirichlet distribution, while Subjective Logic translates these parameters into interpretable belief masses and uncertainty scores. We evaluate the model on the PhysioNet Challenge 2017 dataset, our model achieves an accuracy of 86.12%, an F1 score of 83.14%, a Precision-Recall Area Under the Curve (PR-AUC) of 85.25%, and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 92.87%\u2014outperforming baseline models in three out of four metrics. Critically, the model reduces overconfidence to 0.59% (compared to 12\u201322% in softmax-based baselines), aligning prediction confidence with true accuracy. By progressively increasing the uncertainty threshold u, the model dynamically filters low-confidence predictions, leading to consistently improved performance\u2014reaching up to 93.59% accuracy, 93.22% F1 score, 89.17% PR-AUC, and 95.10% ROC-AUC at u = 0.1. These results validate the model\u2019s capacity for reliable ECG interpretation while leveraging physiological signal symmetry for enhanced feature extraction.<\/jats:p>","DOI":"10.3390\/sym17050794","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T08:41:12Z","timestamp":1747730472000},"page":"794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Reliable Deep Learning Model for ECG Interpretation: Mitigating Overconfidence and Direct Uncertainty Quantification"],"prefix":"10.3390","volume":"17","author":[{"given":"Xuedong","family":"Li","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingxiao","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6323-9420","authenticated-orcid":false,"given":"Shibin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shipeng","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingke","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE7 7XA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Ye","sequence":"additional","affiliation":[{"name":"Coloplast Inc., Chengdu 610074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: Update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1007\/s11277-021-08411-5","article-title":"Chaos theory and ARTFA: Emerging tools for interpreting ECG signals to diagnose cardiac arrhythmias","volume":"118","author":"Gupta","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_3","first-page":"2006","article-title":"ECG frequency domain features extraction: A new characteristic for arrhythmias classification","volume":"Volume 2","author":"Romero","year":"2001","journal-title":"Proceedings of the 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nasiri, J.A., Naghibzadeh, M., Yazdi, H.S., and Naghibzadeh, B. (2009, January 25\u201327). ECG arrhythmia classification with support vector machines and genetic algorithm. Proceedings of the 2009 Third UKSim European Symposium on Computer Modeling and Simulation, Athens, Greece.","DOI":"10.1109\/EMS.2009.39"},{"key":"ref_5","first-page":"892","article-title":"Electrocardiogram classification using reservoir computing with logistic regression","volume":"19","author":"Soriano","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.ijcard.2005.02.007","article-title":"Common errors in computer electrocardiogram interpretation","volume":"106","author":"Guglin","year":"2006","journal-title":"Int. J. Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.jelectrocard.2007.03.008","article-title":"Errors in the computerized electrocardiogram interpretation of cardiac rhythm","volume":"40","author":"Shah","year":"2007","journal-title":"J. Electrocardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., and Manning, C.D. (2015). Effective approaches to attention-based neural machine translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neucom.2020.01.019","article-title":"Toward improving ECG biometric identification using cascaded convolutional neural networks","volume":"391","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schwab, P., Scebba, G.C., Zhang, J., Delai, M., and Karlen, W. (2017, January 24\u201327). Beat by beat: Classifying cardiac arrhythmias with recurrent neural networks. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.363-223"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, M., and Kim, Y. (2018, January 17\u201321). Classification of heart diseases based on ECG signals using long short-term memory. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512761"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"122955","DOI":"10.1109\/ACCESS.2019.2938617","article-title":"Automated heartbeat classification exploiting convolutional neural network with channel-wise attention","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"025005","DOI":"10.1088\/1361-6579\/ab6e55","article-title":"Detection of strict left bundle branch block by neural network and a method to test detection consistency","volume":"41","author":"Yang","year":"2020","journal-title":"Physiol. Meas."},{"key":"ref_18","first-page":"20230002","article-title":"Deep learning models for multilabel ECG abnormalities classification: A comparative study using TPE optimization","volume":"32","author":"Rawi","year":"2023","journal-title":"J. Intell. Syst."},{"key":"ref_19","first-page":"1","article-title":"Evidential deep learning to quantify classification uncertainty","volume":"31","author":"Sensoy","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","unstructured":"Van Amersfoort, J., Smith, L., Teh, Y.W., and Gal, Y. (2020, January 13\u201318). Uncertainty estimation using a single deep deterministic neural network. Proceedings of the International Conference on Machine Learning, Virtual Event."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1109\/TPAMI.2022.3171983","article-title":"Trusted multi-view classification with dynamic evidential fusion","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","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_23","unstructured":"Zhang, W., Di, X., Wei, G., Geng, S., Fu, Z., and Hong, S. (2022). A deep Bayesian neural network for cardiac arrhythmia classification with rejection from ECG recordings. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., An, Z., Zuo, S., Zhu, W., Zhang, Z., Mu, Y., Cao, L., and Garcia, J.D.P. (2022). Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization. Biomed. Signal Process. Control., 73.","DOI":"10.1016\/j.bspc.2021.103424"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Islam, M.F., Zabeen, S., Mehedi, M.H.K., Iqbal, S., and Rasel, A.A. (2022, January 26\u201327). Monte carlo dropout for uncertainty analysis and ecg trace image classification. Proceedings of the Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Montreal, QC, Canada.","DOI":"10.1007\/978-3-031-23028-8_18"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103452","DOI":"10.1109\/ACCESS.2021.3098986","article-title":"An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification","volume":"9","author":"Essa","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40537-025-01070-4","article-title":"Enhancing cardiac diagnostics: A deep learning ensemble approach for precise ECG image classification","volume":"12","author":"Alsayat","year":"2025","journal-title":"J. Big Data"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, B., Liu, Y., Wu, X., Ren, J., and Zhao, Z. (2025). Trustworthy diagnosis of Electrocardiography signals based on out-of-distribution detection. PloS ONE, 20.","DOI":"10.1371\/journal.pone.0317900"},{"key":"ref_29","unstructured":"Wu, Q., Li, H., Li, L., and Yu, Z. (2019). Quantifying intrinsic uncertainty in classification via deep Dirichlet mixture networks. arXiv."},{"key":"ref_30","first-page":"822","article-title":"Fast predictive uncertainty for classification with Bayesian deep networks","volume":"Volume 180","author":"Hobbhahn","year":"2022","journal-title":"Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"125906","DOI":"10.1016\/j.eswa.2024.125906","article-title":"Risk-aware classification via uncertainty quantification","volume":"265","author":"Kaplan","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_32","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_33","unstructured":"Malinin, A., Mlodozeniec, B., and Gales, M. (2019). Ensemble distribution distillation. arXiv."},{"key":"ref_34","first-page":"1","article-title":"Predictive uncertainty estimation via prior networks","volume":"31","author":"Malinin","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","unstructured":"Taboga, M. (2017). Lectures on Probability Theory and Mathematical Statistics, Amazon Publishing."},{"key":"ref_36","unstructured":"Jsang, A. (2018). Subjective Logic: A Formalism for Reasoning Under Uncertainty, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bao, W., Yu, Q., and Kong, Y. (2021, January 11\u201317). Evidential deep learning for open set action recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.01310"},{"key":"ref_38","unstructured":"Higgins, I., Matthey, L., Pal, A., Burgess, C.P., Glorot, X., Botvinick, M.M., Mohamed, S., and Lerchner, A. (2017). beta-vae: Learning basic visual concepts with a constrained variational framework. ICLR, 3."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Clifford, G.D., Liu, C., Moody, B., Li-wei, H.L., Silva, I., Li, Q., Johnson, A., and Mark, R.G. (2017, January 24\u201327). AF classification from a short single lead ECG recording: The PhysioNet\/computing in cardiology challenge 2017. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.065-469"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1161\/CIRCULATIONAHA.121.058911","article-title":"Consumer-led screening for atrial fibrillation: Frontier review of the AF-SCREEN international collaboration","volume":"146","author":"Brandes","year":"2022","journal-title":"Circulation"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1161\/01.CIR.100.8.886","article-title":"ACC\/AHA guidelines for ambulatory electrocardiography: Executive summary and recommendations: A report of the American College of Cardiology\/American Heart Association task force on practice guidelines (committee to revise the guidelines for ambulatory electrocardiography) developed in collaboration with the north American society for pacing and electrophysiology","volume":"100","author":"Crawford","year":"1999","journal-title":"Circulation"},{"key":"ref_42","first-page":"692","article-title":"Research on electrocardiogram classification using deep residual network with pyramid convolution structure","volume":"37","author":"Jiang","year":"2020","journal-title":"J. Biomed. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., and Xu, B. (2016, January 7\u201312). Attention-based bidirectional long short-term memory networks for relation classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zihlmann, M., Perekrestenko, D., and Tschannen, M. (2017, January 24\u201327). Convolutional recurrent neural networks for electrocardiogram classification. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.070-060"},{"key":"ref_46","unstructured":"Goodfellow, I. (2016). Deep Learning, MIT Press."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Karpathy, A., and Fei-Fei, L. (2015, January 7\u201312). Deep visual-semantic alignments for generating image descriptions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S. (2016). Wide residual networks. arXiv.","DOI":"10.5244\/C.30.87"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:35:49Z","timestamp":1760031349000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["sym17050794"],"URL":"https:\/\/doi.org\/10.3390\/sym17050794","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]}}}