{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:08:08Z","timestamp":1778782088918,"version":"3.51.4"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515110936"],"award-info":[{"award-number":["2023A1515110936"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002402","name":"Sun Yat-Sen University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002402","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004000","name":"Guangzhou Municipal Science and Technology Program key projects","doi-asserted-by":"publisher","award":["2025A03J4426"],"award-info":[{"award-number":["2025A03J4426"]}],"id":[{"id":"10.13039\/501100004000","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114570","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:22:06Z","timestamp":1774484526000},"page":"114570","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A frequency-guided denoising framework based on convolutional transformer for electrocardiogram signals"],"prefix":"10.1016","volume":"175","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7094-2843","authenticated-orcid":false,"given":"Mingyue","family":"Cui","sequence":"first","affiliation":[]},{"given":"Yewei","family":"Gan","sequence":"additional","affiliation":[]},{"given":"Jiepeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yanchong","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Daosong","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5539","authenticated-orcid":false,"given":"Yuning","family":"Cui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3347-204X","authenticated-orcid":false,"given":"Kai","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s00186-008-0211-3","article-title":"Cooperation under interval uncertainty","volume":"69","author":"Alparslan-G\u00f6k","year":"2009","journal-title":"Math. Methods Oper. Res."},{"key":"10.1016\/j.engappai.2026.114570_b2","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.bspc.2018.03.003","article-title":"A survey on ECG analysis","volume":"43","author":"Berkaya","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b3","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10479-017-2405-7","article-title":"Voxel-MARS: a method for early detection of alzheimer\u2019s disease by classification of structural brain MRI","volume":"258","author":"\u00c7evik","year":"2017","journal-title":"Ann. Oper. Res."},{"issue":"9","key":"10.1016\/j.engappai.2026.114570_b4","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1049\/iet-spr.2020.0104","article-title":"Review of noise removal techniques in ECG signals","volume":"14","author":"Chatterjee","year":"2020","journal-title":"IET Signal Process."},{"key":"10.1016\/j.engappai.2026.114570_b5","doi-asserted-by":"crossref","first-page":"60806","DOI":"10.1109\/ACCESS.2019.2912036","article-title":"Noise reduction in ECG signals using fully convolutional denoising autoencoders","volume":"7","author":"Chiang","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114570_b6","series-title":"2017 Computing in Cardiology","first-page":"1","article-title":"AF classification from a short single lead ECG recording: The PhysioNet\/computing in cardiology challenge 2017","author":"Clifford","year":"2017"},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b7","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.ejor.2020.01.014","article-title":"Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)","volume":"291","author":"Erku\u015f","year":"2021","journal-title":"European J. Oper. Res."},{"issue":"3","key":"10.1016\/j.engappai.2026.114570_b8","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1016\/j.ejor.2022.03.026","article-title":"On big boss fuzzy interval games","volume":"306","author":"G\u00f6k","year":"2023","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.engappai.2026.114570_b9","series-title":"International Conference on Machine Learning","first-page":"3597","article-title":"SimGANs: Simulator-based generative adversarial networks for ecg synthesis to improve deep ECG classification","author":"Golany","year":"2020"},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1017\/S0266466613000121","article-title":"Estimating the persistence and the autocorrelation function of a time series that is measured with error","volume":"30","author":"Hansen","year":"2014","journal-title":"Econometric Theory"},{"key":"10.1016\/j.engappai.2026.114570_b11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.engappai.2018.12.004","article-title":"Multi-lead model-based ECG signal denoising by guided filter","volume":"79","author":"Hao","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114570_b12","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"779","article-title":"Dual attention convolutional neural network based on adaptive parametric ReLU for denoising ECG signals with strong noise","author":"He","year":"2021"},{"key":"10.1016\/j.engappai.2026.114570_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104964","article-title":"An ECG denoising method based on adversarial denoising convolutional neural network","volume":"84","author":"Hou","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.engappai.2026.114570_b14","first-page":"1","article-title":"Deep neural network denoising model based on sparse representation algorithm for ECG signal","volume":"72","author":"Hou","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114570_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109480","article-title":"A dual-branch convolutional neural network with domain-informed attention for arrhythmia classification of 12-lead electrocardiograms","volume":"139","author":"Jiang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"3","key":"10.1016\/j.engappai.2026.114570_b16","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1109\/JIOT.2021.3093112","article-title":"A multimodal data fusion technique for heartbeat detection in wearable IoT sensors","volume":"9","author":"John","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2026.114570_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2025.170117","article-title":"Next-generation wearable ECG systems: Soft materials, AI integration, and personalized healthcare applications","author":"Khan","year":"2025","journal-title":"Chem. Eng. J."},{"issue":"101","key":"10.1016\/j.engappai.2026.114570_b18","first-page":"1","article-title":"Signal processing techniques for removing noise from ECG signals","volume":"3","author":"Kher","year":"2019","journal-title":"J. Biomed. Eng. Res"},{"key":"10.1016\/j.engappai.2026.114570_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119162","article-title":"A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm","volume":"213","author":"K\u0131yma\u00e7","year":"2023","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b20","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1051\/ro\/2015044","article-title":"Fuzzy prediction strategies for gene-environment networks\u2013fuzzy regression analysis for two-modal regulatory systems","volume":"50","author":"Kropat","year":"2016","journal-title":"RAIRO-Operations Research-Recherche Op\u00e9rationnelle"},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3934\/naco.2018008","article-title":"Fuzzy target-environment networks and fuzzy-regression approaches","volume":"8","author":"Kropat","year":"2018","journal-title":"Numer. Algebra Control Optim."},{"issue":"4","key":"10.1016\/j.engappai.2026.114570_b22","article-title":"Foundations of semialgebraic gene-environment networks.","volume":"7","author":"Kropat","year":"2020","journal-title":"J. Dyn. Games"},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b23","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s10479-017-2496-1","article-title":"Early warning on stock market bubbles via methods of optimization, clustering and inverse problems","volume":"260","author":"K\u00fcr\u00fcm","year":"2018","journal-title":"Ann. Oper. Res."},{"issue":"3","key":"10.1016\/j.engappai.2026.114570_b24","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s10100-011-0224-5","article-title":"A classification problem of credit risk rating investigated and solved by optimisation of the ROC curve","volume":"20","author":"K\u00fcr\u00fcm","year":"2012","journal-title":"Central Eur. J. Oper. Res."},{"key":"10.1016\/j.engappai.2026.114570_b25","series-title":"Computers in Cardiology 1997","first-page":"673","article-title":"A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG","author":"Laguna","year":"1997"},{"key":"10.1016\/j.engappai.2026.114570_b26","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, J., Fu, Y., Zhang, Y., Dou, D., 2023. Spectral enhanced rectangle transformer for hyperspectral image denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5805\u20135814.","DOI":"10.1109\/CVPR52729.2023.00562"},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b27","first-page":"86","article-title":"ECG noise sources and various noise removal techniques: A survey","volume":"5","author":"Limaye","year":"2016","journal-title":"Int. J. Appl. Or Innov. Eng. Manag."},{"key":"10.1016\/j.engappai.2026.114570_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110675","article-title":"Advancing interpretable cardiac disease diagnosis via a transformer-convolutional hybrid network on electrocardiograms","volume":"152","author":"Liu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114570_b29","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"Hierarchical filtering with online learned priors for ECG denoising","author":"Locher","year":"2023"},{"issue":"4","key":"10.1016\/j.engappai.2026.114570_b30","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s12553-022-00662-x","article-title":"Power line noise and baseline wander removal from ECG signals using empirical mode decomposition and lifting wavelet transform technique","volume":"12","author":"Malik","year":"2022","journal-title":"Health Technol."},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b31","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.bspc.2011.03.004","article-title":"A novel method for detecting R-peaks in electrocardiogram (ECG) signal","volume":"7","author":"Manikandan","year":"2012","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b32","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TII.2024.3463707","article-title":"An improved protective relaying technique for transmission line connected with UPFC and DFIG-based wind farm","volume":"21","author":"Mohanty","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.engappai.2026.114570_b33","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":"10.1016\/j.engappai.2026.114570_b34","first-page":"381","article-title":"The MIT-BIH noise stress test database","author":"Moody","year":"1984","journal-title":"Comput. Cardiol."},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b35","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1038\/s41598-023-50334-7","article-title":"Multichannel high noise level ECG denoising based on adversarial deep learning","volume":"14","author":"Mvuh","year":"2024","journal-title":"Sci. Rep."},{"issue":"10440","key":"10.1016\/j.engappai.2026.114570_b36","doi-asserted-by":"crossref","first-page":"2100","DOI":"10.1016\/S0140-6736(24)00367-2","article-title":"Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u20132021: a systematic analysis for the global burden of disease study 2021","volume":"403","author":"Naghavi","year":"2024","journal-title":"Lancet"},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b37","doi-asserted-by":"crossref","first-page":"428","DOI":"10.3934\/jimo.2023084","article-title":"Peer group situations and games with fuzzy uncertainty","volume":"20","author":"\u00d6zcan","year":"2024","journal-title":"J. Ind. Manag. Optim."},{"issue":"12","key":"10.1016\/j.engappai.2026.114570_b38","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1080\/02331934.2016.1209672","article-title":"Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty","volume":"66","author":"\u00d6zmen","year":"2017","journal-title":"Optimization"},{"issue":"12","key":"10.1016\/j.engappai.2026.114570_b39","doi-asserted-by":"crossref","first-page":"4780","DOI":"10.1016\/j.cnsns.2011.04.001","article-title":"RCMARS: Robustification of CMARS with different scenarios under polyhedral uncertainty set","volume":"16","author":"\u00d6zmen","year":"2011","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"10.1016\/j.engappai.2026.114570_b40","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.eneco.2018.01.022","article-title":"Natural gas consumption forecast with MARS and CMARS models for residential users","volume":"70","author":"\u00d6zmen","year":"2018","journal-title":"Energy Econ."},{"key":"10.1016\/j.engappai.2026.114570_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108123","article-title":"A new automated compression technique for 2D electrocardiogram signals using discrete wavelet transform","volume":"133","author":"Pal","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114570_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106414","article-title":"Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model","volume":"123","author":"Rahman","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114570_b43","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.bspc.2017.09.020","article-title":"An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter","volume":"40","author":"Rakshit","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.engappai.2026.114570_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103275","article-title":"A deep learning-based framework for ECG signal denoising based on stacked cardiac cycle tensor","volume":"71","author":"Rasti-Meymandi","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"9","key":"10.1016\/j.engappai.2026.114570_b45","first-page":"221","article-title":"ECG De-Noising using improved thresholding based on wavelet transforms","volume":"9","author":"Reddy","year":"2009","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"10.1016\/j.engappai.2026.114570_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.annemergmed.2024.11.019","article-title":"ECG patterns of occlusion myocardial infarction: a narrative review","author":"Ricci","year":"2025","journal-title":"Ann. Emerg. Med."},{"key":"10.1016\/j.engappai.2026.114570_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106484","article-title":"ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG","volume":"124","author":"Roy","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b48","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1038\/s41467-022-29153-3","article-title":"Automated multilabel diagnosis on electrocardiographic images and signals","volume":"13","author":"Sangha","year":"2022","journal-title":"Nat. Commun."},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b49","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1007\/s10957-017-1159-3","article-title":"A stochastic maximum principle for a markov regime-switching jump-diffusion model with delay and an application to finance","volume":"179","author":"Savku","year":"2018","journal-title":"J. Optim. Theory Appl."},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b50","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s10479-020-03768-5","article-title":"Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market","volume":"312","author":"Savku","year":"2022","journal-title":"Ann. Oper. Res."},{"issue":"6","key":"10.1016\/j.engappai.2026.114570_b51","doi-asserted-by":"crossref","first-page":"2041","DOI":"10.1109\/TBME.2021.3135154","article-title":"Reducing line-of-block artifacts in cardiac activation maps estimated using ECG imaging: A comparison of source models and estimation methods","volume":"69","author":"Schuler","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.engappai.2026.114570_b52","series-title":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"4546","article-title":"Sparsity-assisted signal smoothing (revisited)","author":"Selesnick","year":"2017"},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b53","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/TCBB.2020.2976981","article-title":"A new ECG denoising framework using generative adversarial network","volume":"18","author":"Singh","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"5\u20136","key":"10.1016\/j.engappai.2026.114570_b54","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/02331930701618740","article-title":"New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology","volume":"56","author":"Taylan","year":"2007","journal-title":"Optimization"},{"issue":"1","key":"10.1016\/j.engappai.2026.114570_b55","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.camwa.2010.04.040","article-title":"On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization","volume":"60","author":"Taylan","year":"2010","journal-title":"Comput. Math. Appl."},{"issue":"2","key":"10.1016\/j.engappai.2026.114570_b56","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s11750-010-0155-7","article-title":"A new approach to multivariate adaptive regression splines by using Tikhonov regularization and continuous optimization","volume":"18","author":"Taylan","year":"2010","journal-title":"Top"},{"issue":"13\u201315","key":"10.1016\/j.engappai.2026.114570_b57","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1080\/02664763.2020.1864815","article-title":"A new outlier detection method based on convex optimization: application to diagnosis of Parkinson\u2019s disease","volume":"48","author":"Taylan","year":"2021","journal-title":"J. Appl. Stat."},{"issue":"3","key":"10.1016\/j.engappai.2026.114570_b58","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s11831-021-09642-2","article-title":"A review on computational methods for denoising and detecting ecg signals to detect cardiovascular diseases","volume":"29","author":"Tripathi","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"issue":"7","key":"10.1016\/j.engappai.2026.114570_b59","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1109\/JBHI.2022.3169325","article-title":"An ECG signal denoising method using conditional generative adversarial net","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.engappai.2026.114570_b60","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/6737102","article-title":"Deep convolutional generative adversarial network with LSTM for ecg denoising","volume":"2023","author":"Wang","year":"2023","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.engappai.2026.114570_b61","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.engappai.2016.02.015","article-title":"ECG signal enhancement based on improved denoising auto-encoder","volume":"52","author":"Xiong","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114570_b62","first-page":"1","article-title":"An ECG denoising method based on the generative adversarial residual network","volume":"2021","author":"Xu","year":"2021","journal-title":"Comput. Math. Methods Med."},{"issue":"18","key":"10.1016\/j.engappai.2026.114570_b63","first-page":"3","article-title":"Discrete tomography: a joint contribution by optimization, equivariance analysis and learning","author":"Ya\u015far","year":"2006","journal-title":"CASYS. Int. J. Comput. Anticip. Syst."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008511?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008511?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:12:29Z","timestamp":1778778749000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626008511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":63,"alternative-id":["S0952197626008511"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114570","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A frequency-guided denoising framework based on convolutional transformer for electrocardiogram signals","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114570","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114570"}}