{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:18:19Z","timestamp":1783120699883,"version":"3.54.6"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100019404","name":"Shenzhen-Hong Kong Institute of Brain Science","doi-asserted-by":"publisher","award":["2023SHIBS0003"],"award-info":[{"award-number":["2023SHIBS0003"]}],"id":[{"id":"10.13039\/501100019404","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005329","name":"Natural Science Foundation of Guizhou Province","doi-asserted-by":"publisher","award":["[2026]-929"],"award-info":[{"award-number":["[2026]-929"]}],"id":[{"id":"10.13039\/501100005329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["ZDSYS20230626091203008"],"award-info":[{"award-number":["ZDSYS20230626091203008"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["RCBS20231211090800003"],"award-info":[{"award-number":["RCBS20231211090800003"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110399","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:41:11Z","timestamp":1777592471000},"page":"110399","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CWANet: Class-wise alignment for enhanced cross-patient generalizability in ECG classification"],"prefix":"10.1016","volume":"122","author":[{"given":"Ziqiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-0857","authenticated-orcid":false,"given":"Zhenxi","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihui","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guofa","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luping","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunxiao","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.bspc.2026.110399_b1","doi-asserted-by":"crossref","first-page":"152","DOI":"10.3390\/bioengineering9040152","article-title":"A hybrid deep learning approach for ECG-based arrhythmia classification","volume":"9","author":"Madan","year":"2022","journal-title":"Bioengineering"},{"key":"10.1016\/j.bspc.2026.110399_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102379","article-title":"Automatic diagnosis of arrhythmia with electrocardiogram using multiple instance learning: From rhythm annotation to heartbeat prediction","volume":"132","author":"Zhang","year":"2022","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b3","doi-asserted-by":"crossref","first-page":"8804","DOI":"10.1038\/s41598-024-59311-0","article-title":"Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA","volume":"14","author":"Zhou","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b4","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s11760-023-02737-2","article-title":"ECG heartbeats classification with dilated convolutional autoencoder","volume":"18","author":"Arslan","year":"2024","journal-title":"Signal Image Video Process."},{"issue":"4","key":"10.1016\/j.bspc.2026.110399_b5","article-title":"An intelligent learning approach for improving ECG signal classification and arrhythmia analysis","volume":"15","author":"Allabun","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.bspc.2026.110399_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106211","article-title":"Cat-net: Convolution, attention, and transformer based network for single-lead ecg arrhythmia classification","volume":"93","author":"Islam","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110399_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101978","article-title":"Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram","volume":"101","author":"Barandas","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.bspc.2026.110399_b8","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.inffus.2019.06.024","article-title":"Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network","volume":"53","author":"Yao","year":"2020","journal-title":"Inf. Fusion"},{"issue":"9","key":"10.1016\/j.bspc.2026.110399_b9","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1109\/JBHI.2020.2981526","article-title":"Deep multi-scale fusion neural network for multi-class arrhythmia detection","volume":"24","author":"Wang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"12","key":"10.1016\/j.bspc.2026.110399_b10","doi-asserted-by":"crossref","first-page":"5618","DOI":"10.3390\/s23125618","article-title":"Automated signal quality assessment of single-lead ecg recordings for early detection of silent atrial fibrillation","volume":"23","author":"Lueken","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110399_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104064","article-title":"A novel attentional deep neural network-based assessment method for ECG quality","volume":"79","author":"Jin","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"4","key":"10.1016\/j.bspc.2026.110399_b12","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.3390\/s23042288","article-title":"A deep learning architecture using 3D vectorcardiogram to detect R-peaks in ECG with enhanced precision","volume":"23","author":"Mehri","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110399_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102337","article-title":"Multi-modal heart failure risk estimation based on short ECG and sampled long-term HRV","volume":"107","author":"Gonz\u00e1lez","year":"2024","journal-title":"Inf. Fusion"},{"issue":"7","key":"10.1016\/j.bspc.2026.110399_b14","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1038\/s41591-023-02396-3","article-title":"Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction","volume":"29","author":"Al-Zaiti","year":"2023","journal-title":"Nature Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b15","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."},{"key":"10.1016\/j.bspc.2026.110399_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.bios.2024.117073","article-title":"Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability and generalization constraints","volume":"271","author":"Ding","year":"2025","journal-title":"Biosens. Bioelectron."},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b17","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":"Nature Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b18","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1038\/s41467-020-15432-4","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b19","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1038\/s41467-024-44930-y","article-title":"Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts","volume":"15","author":"Chen","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2026.110399_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101978","article-title":"Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram","volume":"101","author":"Barandas","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.bspc.2026.110399_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106265","article-title":"A deep learning approach for inter-patient classification of premature ventricular contraction from electrocardiogram","volume":"94","author":"Wang","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110399_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106160","article-title":"Deep neural networks generalization and fine-tuning for 12-lead ECG classification","volume":"93","author":"Avetisyan","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110399_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103363","article-title":"ECGFM: A foundation model for ECG analysis trained on a multi-center million-ECG dataset","author":"Zhang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.bspc.2026.110399_b24","first-page":"1","article-title":"MaeFE: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning","volume":"72","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.bspc.2026.110399_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122960","article-title":"Cross-database and cross-channel electrocardiogram arrhythmia heartbeat classification based on unsupervised domain adaptation","volume":"244","author":"Imtiaz","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110399_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107275","article-title":"Diagnosis of atrial fibrillation based on unsupervised domain adaptation","volume":"164","author":"Du","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110399_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126824","article-title":"Class-specific weighted broad learning system-based domain adaptation for patient-specific ECG classification","volume":"273","author":"Fan","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"10.1016\/j.bspc.2026.110399_b28","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1109\/JBHI.2024.3524085","article-title":"TCGAN: Temporal convolutional generative adversarial network for fetal ECG extraction using single-channel abdominal ECG","volume":"29","author":"Huang","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110399_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2025.103162","article-title":"ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks","volume":"167","author":"Simone","year":"2025","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.110399_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105149","article-title":"A novel Dual-Branch Generative Adversarial Network for electrocardiogram data generation","volume":"162","author":"Zhou","year":"2025","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.bspc.2026.110399_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112260","article-title":"HQ-DCGAN: Hybrid quantum deep convolutional generative adversarial network approach for ECG generation","volume":"301","author":"Qu","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"7","key":"10.1016\/j.bspc.2026.110399_b32","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.70070","article-title":"Uncertainty-guided diffusion model for high-fidelity ECG synthesis and classification","volume":"42","author":"Zhang","year":"2025","journal-title":"Expert Syst."},{"issue":"2299","key":"10.1016\/j.bspc.2026.110399_b33","article-title":"Reconstructing ECG from indirect signals: a denoising diffusion approach","volume":"383","author":"Bedin","year":"2025","journal-title":"Philoso. Trans. R. Soc. A-Math. Phys. Eng. Sci."},{"key":"10.1016\/j.bspc.2026.110399_b34","first-page":"84409","article-title":"Leveraging an ECG beat diffusion model for morphological reconstruction from indirect signals","volume":"vol. 37","author":"Bedin","year":"2024"},{"key":"10.1016\/j.bspc.2026.110399_b35","doi-asserted-by":"crossref","first-page":"75818","DOI":"10.1109\/ACCESS.2023.3296542","article-title":"Synthetic ECG signal generation using probabilistic diffusion models","volume":"11","author":"Adib","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110399_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106465","article-title":"Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning","volume":"153","author":"Liu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110399_b37","doi-asserted-by":"crossref","first-page":"61410","DOI":"10.1109\/ACCESS.2022.3181727","article-title":"Meta structural learning algorithm with interpretable convolutional neural networks for arrhythmia detection of multisession ECG","volume":"10","author":"Meqdad","year":"2022","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.bspc.2026.110399_b38","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/JBHI.2023.3247861","article-title":"Few-shot class-incremental learning for medical time series classification","volume":"28","author":"Sun","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110399_b39","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":"10.1016\/j.bspc.2026.110399_b40","series-title":"A large scale 12-lead electrocardiogram database for arrhythmia study. PhysioNet","author":"Zheng","year":"2022"},{"issue":"7","key":"10.1016\/j.bspc.2026.110399_b41","first-page":"1368","article-title":"An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection","volume":"8","author":"Liu","year":"2018","journal-title":"J. Med. Imaging Health Inform."},{"issue":"7","key":"10.1016\/j.bspc.2026.110399_b42","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","article-title":"Automatic classification of heartbeats using ECG morphology and heartbeat interval features","volume":"51","author":"De Chazal","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110399_b43","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110399_b44","series-title":"Supervised contrastive learning","author":"Khosla","year":"2020"},{"key":"10.1016\/j.bspc.2026.110399_b45","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.bspc.2026.110399_b46","series-title":"Super-convergence: Very fast training of neural networks using large learning rates","author":"Smith","year":"2018"},{"issue":"5","key":"10.1016\/j.bspc.2026.110399_b47","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/JBHI.2020.3022989","article-title":"Deep learning for ECG analysis: Benchmarks and insights from PTB-XL","volume":"25","author":"Strodthoff","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110399_b48","series-title":"PhysioWave: A multi-scale wavelet-transformer for physiological signal representation","author":"Chen","year":"2025"},{"key":"10.1016\/j.bspc.2026.110399_b49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jspi.2021.01.001","article-title":"Fitting time series models for longitudinal surveys with nonignorable missing data","volume":"214","author":"Liu","year":"2021","journal-title":"J. Statist. Plann. Inference"},{"issue":"1","key":"10.1016\/j.bspc.2026.110399_b50","doi-asserted-by":"crossref","first-page":"14485","DOI":"10.1038\/s41598-022-18664-0","article-title":"Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110399_b51","first-page":"1000","article-title":"An ECG classification method based on multi-task learning and cot attention mechanism","volume":"vol. 11","author":"Geng","year":"2023"},{"issue":"2","key":"10.1016\/j.bspc.2026.110399_b52","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/JBHI.2022.3221464","article-title":"Detection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategy","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110399_b53","article-title":"An automated cardiac arrhythmia classification network for 45 arrhythmia classes using 12-lead electrocardiogram","author":"Kim","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110399_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2025.122273","article-title":"ECG-STAR: Spatio-temporal attention residual networks for multi-label ECG abnormality classification","volume":"717","author":"Liu","year":"2025","journal-title":"Inform. Sci."},{"issue":"12","key":"10.1016\/j.bspc.2026.110399_b55","doi-asserted-by":"crossref","DOI":"10.3390\/sym12122019","article-title":"Classification of congestive heart failure from ECG segments with a multi-scale residual network","volume":"12","author":"Li","year":"2020","journal-title":"Symmetry"},{"issue":"5","key":"10.1016\/j.bspc.2026.110399_b56","doi-asserted-by":"crossref","DOI":"10.3390\/s21051906","article-title":"Detection of myocardial infarction using ECG and multi-scale feature concatenate","volume":"21","author":"Jian","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110399_b57","series-title":"2020 Computing in Cardiology","first-page":"1","article-title":"SE-ECGNet: multi-scale SE-net for multi-lead ECG data","author":"Chen","year":"2020"},{"key":"10.1016\/j.bspc.2026.110399_b58","article-title":"Multi-scale and multi-channel information fusion for exercise electrocardiogram feature extraction and classification","author":"Zhang","year":"2024","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.bspc.2026.110399_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e26147","article-title":"Enhancing ECG classification with continuous wavelet transform and multi-branch transformer","volume":"10","author":"Qiu","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.bspc.2026.110399_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105060","article-title":"A deep neural network based on multi-model and multi-scale for arrhythmia classification","volume":"85","author":"Jiang","year":"2023","journal-title":"Biomed. Signal Process. Control."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009535?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009535?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:33:15Z","timestamp":1783117995000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":60,"alternative-id":["S1746809426009535"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110399","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CWANet: Class-wise alignment for enhanced cross-patient generalizability in ECG classification","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110399","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"110399"}}