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To solve this problem, we propose a novel network which integrates hybrid transformer and multi-receptive feature extraction mechanism into score-based diffusion model. We used score-based diffusion model to reconstruct the clean ECG signals from noisy ones. The experiment was conducted on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of our method. Baseline methods are used for comparison. The evaluation results show that our method can achieve an outstanding performance on four distance-based evaluation metrics by at least 26% overall improvement in the comparison with the best baseline method. The study demonstrates that the signal denoising and reconstruction method based on the self-designed score-based diffusion model can effectively remove the interferences in the ECG signals, thereby facilitating the subsequent diagnosis in real-world situation. It also has huge potential for establishing the ECG intelligent analysis system.<\/jats:p>","DOI":"10.1145\/3744654","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T12:26:12Z","timestamp":1751372772000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated Hybrid Transformer and Multi-Receptive Feature Extraction Mechanism for Electrocardiogram Denoising Using Score-Based Diffusion Model"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8711-4462","authenticated-orcid":false,"given":"Baofeng","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China and Neusoft Research of Intelligent Healthcare Technology, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2411-8016","authenticated-orcid":false,"given":"Wanjun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Neusoft Research of Intelligent Healthcare Technology, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2199-1088","authenticated-orcid":false,"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China and Neusoft Corporation, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4006-2000","authenticated-orcid":false,"given":"Jiren","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China and Neusoft Corporation, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Cardiovascular Diseases (cvds). 2020. 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Retrieved from https:\/\/arxiv.org\/abs\/2310.06625"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1109\/ICETET.2008.248","volume-title":"Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology","author":"Sabarimalai Manikandan M.","year":"2008","unstructured":"M. Sabarimalai Manikandan and Samarendra Dandapat. 2008. ECG distortion measures and their effectiveness. In Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology. IEEE, 705\u2013710."},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"102236","DOI":"10.1016\/j.artmed.2022.102236","article-title":"Enhancing dynamic ECG heartbeat classification with lightweight transformer model","volume":"124","author":"Meng Lingxiao","year":"2022","unstructured":"Lingxiao Meng, Wenjun Tan, Jiangang Ma, Ruofei Wang, Xiaoxia Yin, and Yanchun Zhang. 2022. 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Expert Systems with Applications 203 (2022), 117206.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.eswa.2016.09.030","article-title":"ECG databases for biometric systems: A systematic review","volume":"67","author":"Merone Mario","year":"2017","unstructured":"Mario Merone, Paolo Soda, Mario Sansone, and Carlo Sansone. 2017. ECG databases for biometric systems: A systematic review. Expert Systems with Applications 67 (2017), 189\u2013202.","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"e_1_3_1_35_2","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 George B.","year":"2001","unstructured":"George B. Moody and Roger G. Mark. 2001. The impact of the MIT-BIH arrhythmia database. 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PMLR, 8162\u20138171."},{"issue":"1","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/10.900246","article-title":"A rate distortion optimal ECG coding algorithm","volume":"48","author":"Nygaard Ranveig","year":"2001","unstructured":"Ranveig Nygaard, Gerry Melnikov, and Aggelos K. Katsaggelos. 2001. A rate distortion optimal ECG coding algorithm. IEEE Transactions on Biomedical Engineering 48, 1 (2001), 28\u201340.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"e_1_3_1_39_2","first-page":"2337","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Park Taesung","year":"2019","unstructured":"Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. 2019. Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2337\u20132346."},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"106441","DOI":"10.1016\/j.bspc.2024.106441","article-title":"A deep learning framework for ECG denoising and classification","volume":"94","author":"Peng Huyang","year":"2024","unstructured":"Huyang Peng, Xiaohan Chang, Zhenjie Yao, Donglin Shi, and Yongrui Chen. 2024. A deep learning framework for ECG denoising and classification. Biomedical Signal Processing and Control 94 (2024), 106441.","journal-title":"Biomedical Signal Processing and Control"},{"key":"e_1_3_1_41_2","first-page":"705","volume-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Pinaya Walter H. L.","year":"2022","unstructured":"Walter H. L. Pinaya, Mark S. Graham, Robert Gray, Pedro F. Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H. Mah, Andrew D. MacKinnon, James T. Teo, Rolf Jager, et\u00a0al. 2022. Fast unsupervised brain anomaly detection and segmentation with diffusion models. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 705\u2013714."},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"102992","DOI":"10.1016\/j.bspc.2021.102992","article-title":"DeepFilter: An ECG baseline wander removal filter using deep learning techniques","volume":"70","author":"Romero Francisco P.","year":"2021","unstructured":"Francisco P. Romero, David C. Pi\u00f1ol, and Carlos R. V\u00e1zquez-Seisdedos. 2021. DeepFilter: An ECG baseline wander removal filter using deep learning techniques. Biomedical Signal Processing and Control 70 (2021), 102992.","journal-title":"Biomedical Signal Processing and Control"},{"key":"e_1_3_1_43_2","unstructured":"Francisco Perdig\u00f3n Romero Liset V\u00e1zquez Romaguera Carlos Rom\u00e1n V\u00e1zquez-Seisdedos C\u00edcero Ferreira Fernandes Costa Marly Guimar\u00e3es Fernandes Costa Jo\u00e3o Evangelista Neto. 2018. Baseline wander removal methods for ECG signals: A comparative study. arXiv:1807.11359. Retrieved from https:\/\/arxiv.org\/abs\/1807.11359"},{"key":"e_1_3_1_44_2","first-page":"2256","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International Conference on Machine Learning. PMLR, 2256\u20132265."},{"key":"e_1_3_1_45_2","unstructured":"Jiaming Song Chenlin Meng and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv:2010.02502. Retrieved from https:\/\/arxiv.org\/abs\/2010.02502"},{"key":"e_1_3_1_46_2","first-page":"11918","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems 32 (2019), 11918\u201311930.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_47_2","article-title":"Association for the Advancement of Medical Instrumentation","volume":"41","author":"Suite","year":"2016","unstructured":"Suite and Arlington. 2016. Association for the Advancement of Medical Instrumentation. Journal of Clinical Engineering 41 (2016).","journal-title":"Journal of Clinical Engineering"},{"key":"e_1_3_1_48_2","first-page":"1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Szegedy Christian","year":"2015","unstructured":"Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1\u20139."},{"issue":"9","key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/oxfordjournals.eurheartj.a060332","article-title":"The European ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography","volume":"13","author":"Taddei A.","year":"1992","unstructured":"A. Taddei, G. Distante, M. Emdin, P. Pisani, G. B. Moody, C. Zeelenberg, and C. Marchesi. 1992. The European ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European Heart Journal 13, 9 (1992), 1164\u20131172.","journal-title":"European Heart Journal"},{"key":"e_1_3_1_50_2","first-page":"14","article-title":"ECG baseline wander elimination using wavelet packets","volume":"3","author":"Mozaffary Behzad","year":"2005","unstructured":"Behzad Mozaffary and Mohammad A. Tinati. 2005. ECG baseline wander elimination using wavelet packets. World Academy of Science, Engineering and Technology 3 (2005), 14\u201316.","journal-title":"World Academy of Science, Engineering and Technology"},{"key":"e_1_3_1_51_2","first-page":"6306","article-title":"Neural discrete representation learning","volume":"30","author":"Van Den Oord Aaron","year":"2017","unstructured":"Aaron Van Den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017. Neural discrete representation learning. 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