{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:23:58Z","timestamp":1783095838387,"version":"3.54.6"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100019024","name":"Guangdong Polytechnic Normal University","doi-asserted-by":"publisher","award":["2022SDKYA002"],"award-info":[{"award-number":["2022SDKYA002"]}],"id":[{"id":"10.13039\/100019024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515110078"],"award-info":[{"award-number":["2021A1515110078"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Signal Processing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.dsp.2026.106167","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T14:44:58Z","timestamp":1777214698000},"page":"106167","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Adaptive gated temporal-aware multilayer broad learning system for ECG classification via uncertainty-driven transfer"],"prefix":"10.1016","volume":"180","author":[{"given":"Xiao-Li","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng-Hai","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8912-9542","authenticated-orcid":false,"given":"Qi","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.dsp.2026.106167_bib0001","unstructured":"World Health Organization (WHO), Cardiovascular diseases (CVDs), 2023, https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-%28cvds%29."},{"issue":"1","key":"10.1016\/j.dsp.2026.106167_bib0002","first-page":"1","article-title":"Arrhythmia-related mortalities in the united states (1968-2021)","volume":"81","author":"Chugh","year":"2023","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.dsp.2026.106167_bib0003","doi-asserted-by":"crossref","first-page":"685","DOI":"10.2147\/VHRM.S508620","article-title":"Deep learning-based detection of arrhythmia using ECG signals - a comprehensive review","volume":"21","author":"Reshad","year":"2025","journal-title":"Vasc. Health Risk Manag."},{"key":"10.1016\/j.dsp.2026.106167_bib0004","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2025.1590170","article-title":"The most common errors in automatic ECG interpretation","volume":"16","author":"Kraik","year":"2025","journal-title":"Front Physiol"},{"key":"10.1016\/j.dsp.2026.106167_bib0005","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.3390\/diagnostics15151950","article-title":"Interpretable deep learning models for arrhythmia classification based on ECG signals using PTB-X dataset","volume":"15","author":"Atwa","year":"2025","journal-title":"Diagnostics"},{"key":"10.1016\/j.dsp.2026.106167_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.irbm.2022.07.001","article-title":"Therapeutic ultrasound applications in cardiovascular diseases: a review","volume":"44","author":"Ditac","year":"2023","journal-title":"IRBM"},{"key":"10.1016\/j.dsp.2026.106167_bib0007","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s40031-022-00831-6","article-title":"ECG Signal analysis based on the spectrogram and spider monkey optimisation technique","volume":"104","author":"Gupta","year":"2023","journal-title":"J. Inst. Eng. B"},{"key":"10.1016\/j.dsp.2026.106167_bib0008","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-78028-8","article-title":"Deep learning hybrid model ECG classification using alexnet and parallel dual branch fusion network model","volume":"14","author":"Kolhar","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.dsp.2026.106167_bib0009","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s12938-025-01349-w","article-title":"Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management","volume":"24","author":"Wu","year":"2025","journal-title":"BioMed Eng. OnLine"},{"issue":"4","key":"10.1016\/j.dsp.2026.106167_bib0010","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.irbm.2019.12.001","article-title":"Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey","volume":"41","author":"Sahoo","year":"2020","journal-title":"IRBM"},{"issue":"1","key":"10.1016\/j.dsp.2026.106167_bib0011","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.irbm.2020.06.004","article-title":"Cardiovascular disorder severity detection using myocardial anatomic features based optimized extreme learning machine approach","volume":"43","author":"Muthulakshmi","year":"2022","journal-title":"IRBM"},{"issue":"4","key":"10.1016\/j.dsp.2026.106167_bib0012","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.dsp.2026.106167_bib0013","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2019.02.016","article-title":"Classification of myocardial infarction with multi-lead ECG signals and deep CNN","volume":"122","author":"Baloglu","year":"2019","journal-title":"Pattern Recognit. Lett."},{"issue":"12","key":"10.1016\/j.dsp.2026.106167_bib0014","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1587\/transcom.2020SEP0002","article-title":"ECG classification with multi-scale deep features based on adaptive beat-segmentation","volume":"E103-B","author":"Sun","year":"2020","journal-title":"IEICE Trans. Commun."},{"issue":"B","key":"10.1016\/j.dsp.2026.106167_bib0015","article-title":"Arrhythmia classification of LSTM autoencoder based on time series anomaly detection","volume":"71","author":"Liu","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"10.1016\/j.dsp.2026.106167_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.irbm.2022.09.003","article-title":"MonEco: a novel health monitoring ecosystem to predict respiratory and cardiovascular disorders","volume":"44","author":"Lazazzera","year":"2023","journal-title":"IRBM"},{"issue":"2","key":"10.1016\/j.dsp.2026.106167_bib0017","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.irbm.2013.01.006","article-title":"SVELTE: Evaluation device of energy expenditure and physical condition for the prevention and treatment of obesity-related diseases through the analysis of a person\u2019s physical activities","volume":"34","author":"Doron","year":"2013","journal-title":"IRBM"},{"key":"10.1016\/j.dsp.2026.106167_bib0018","series-title":"Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"1915","article-title":"Lightweight convolutional neural network for real-time arrhythmia classification on low-power wearable electrocardiograph","author":"Kim","year":"2022"},{"key":"10.1016\/j.dsp.2026.106167_bib0019","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.860032","article-title":"Deep learning for detecting and locating myocardial infarction by electrocardiogram: a literature review","volume":"9","author":"Xiong","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.dsp.2026.106167_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111412","article-title":"Imbalanced ECG data classification using a novel model based on active training subset selection and modified broad learning system","volume":"198","author":"Fan","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106167_bib0021","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.ins.2023.02.048","article-title":"Factorization of broad expansion for broad learning system","volume":"630","author":"Ma","year":"2023","journal-title":"Inf. Sci."},{"issue":"24","key":"10.1016\/j.dsp.2026.106167_bib0022","article-title":"Classification of electrocardiogram signals based on hybrid deep learning models","volume":"14","author":"Zhang","year":"2022","journal-title":"Sustainability"},{"issue":"8","key":"10.1016\/j.dsp.2026.106167_bib0023","doi-asserted-by":"crossref","first-page":"4964","DOI":"10.3390\/app13084964","article-title":"Deep learning-based ECG arrhythmia classification","volume":"13","author":"Xiao","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.dsp.2026.106167_bib0024","article-title":"Temporal enhanced broad learning system for time series data","volume":"228","author":"Wang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.dsp.2026.106167_bib0025","article-title":"Adaptive gating mechanism in broad learning system for robust classification","volume":"121","author":"Chen","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.dsp.2026.106167_bib0026","article-title":"Partial multiview incomplete multilabel learning via uncertainty-Driven reliable dynamic fusion","author":"Wen","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106167_bib0027","article-title":"A systematic review of hyperparameter optimization methods: metaheuristic, statistical, sequential and numerical approaches","volume":"7","author":"Raiaan","year":"2024","journal-title":"Digit. Appl. Archaeol. Cult. Herit."},{"key":"10.1016\/j.dsp.2026.106167_bib0028","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1484","article-title":"Hyperparameter optimization: foundations, algorithms and applications","author":"Bischl","year":"2023","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"10.1016\/j.dsp.2026.106167_bib0029","series-title":"Proceedings of the 35Th UAI Conference","first-page":"788","article-title":"Practical multi-fidelity bayesian optimization for hyperparameter tuning","author":"Wu","year":"2020"},{"issue":"3","key":"10.1016\/j.dsp.2026.106167_bib0030","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.3390\/app15031018","article-title":"Bayesian hyperparameter optimization of machine learning algorithms for biomass gasification","volume":"15","author":"Cihan","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.dsp.2026.106167_bib0031","unstructured":"J. Yoon, et al., Domain Generalization for Medical Image Analysis: A Review, 2023, (arXiv preprint). https:\/\/arxiv.org\/abs\/2310.08598."},{"key":"10.1016\/j.dsp.2026.106167_bib0032","unstructured":"Z. Niu, et al., A Survey on Domain Generalization for Medical Image Analysis, 2024, arXiv: 2402.05035."},{"key":"10.1016\/j.dsp.2026.106167_bib0033","doi-asserted-by":"crossref","unstructured":"S. Matta, et al., A systematic review of generalization research in medical image classification, (2024). https:\/\/arxiv.org\/abs\/2403.12167.","DOI":"10.1016\/j.compbiomed.2024.109256"},{"key":"10.1016\/j.dsp.2026.106167_bib0034","doi-asserted-by":"crossref","unstructured":"M.N. Imtiaz, N. Khan, Cross-Database and cross-Channel ECG arrhythmia heartbeat classification based on unsupervised domain adaptation, (2023). 10.48550\/arXiv.2306.04433.","DOI":"10.1016\/j.eswa.2023.122960"},{"key":"10.1016\/j.dsp.2026.106167_bib0035","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.3390\/app15105535","article-title":"Cross-Database learning framework for electrocardiogram arrhythmia classification using two-Dimensional beat-Score-Map representation","volume":"15","author":"Lee","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.dsp.2026.106167_bib0036","unstructured":"J.F. N\u00fa\u00f1ez, et al., Synthetic ECG generation for data augmentation and transfer learning in arrhythmia classification, (2024). 10.48550\/arXiv.2411.18456."},{"key":"10.1016\/j.dsp.2026.106167_bib0037","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1186\/s13634-024-01197-1","article-title":"Deep learning-assisted arrhythmia classification using 2-D ECG spectrograms","volume":"2024","author":"Malleswari","year":"2024","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"6","key":"10.1016\/j.dsp.2026.106167_bib0038","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.irbm.2019.10.001","article-title":"R-Peak detection using chaos analysis in standard and real-time ECG databases","volume":"40","author":"Gupta","year":"2019","journal-title":"IRBM"},{"issue":"5","key":"10.1016\/j.dsp.2026.106167_bib0039","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.irbm.2019.12.002","article-title":"Isolation of fetal ECG signals from abdominal ECG using wavelet analysis","volume":"41","author":"Alshebly","year":"2020","journal-title":"IRBM"},{"issue":"4","key":"10.1016\/j.dsp.2026.106167_bib0040","doi-asserted-by":"crossref","first-page":"4219","DOI":"10.1080\/03772063.2023.2202162","article-title":"Pre-processing based ECG signal analysis using emerging tools","volume":"70","author":"Gupta","year":"2024","journal-title":"IETE J. Res."},{"issue":"3","key":"10.1016\/j.dsp.2026.106167_bib0041","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.irbm.2019.04.004","article-title":"HMM-based supervised machine learning framework for the detection of fECG R-R peak locations","volume":"40","author":"Huque","year":"2019","journal-title":"IRBM"},{"key":"10.1016\/j.dsp.2026.106167_bib0042","series-title":"ACL","article-title":"One network, many masks: towards more parameter-efficient transfer learning","author":"Zeng","year":"2023"},{"key":"10.1016\/j.dsp.2026.106167_bib0043","article-title":"Lora: low-Rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"Int. Conf. Mach. Learn."},{"key":"10.1016\/j.dsp.2026.106167_bib0044","series-title":"IJCNN","article-title":"Temporal convolutional attention neural networks for time series forecasting","author":"Lin","year":"2021"},{"issue":"10","key":"10.1016\/j.dsp.2026.106167_bib0045","first-page":"1345","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE TKDE"},{"issue":"4","key":"10.1016\/j.dsp.2026.106167_bib0046","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/TCYB.2018.2863020","article-title":"Recurrent broad learning systems for time series prediction","volume":"50","author":"Xu","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.dsp.2026.106167_bib0047","series-title":"Proceedings of the 57Th Annual Meeting of the Association for Computational Linguistics (ACL)","first-page":"2979","article-title":"Transformer-XL: attentive language models beyond a fixed-Length context","author":"Dai","year":"2019"},{"key":"10.1016\/j.dsp.2026.106167_bib0048","series-title":"Advances in Neural Information Processing Systems 27 (NIPS 2014)","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014"},{"issue":"3","key":"10.1016\/j.dsp.2026.106167_bib0049","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning","volume":"110","author":"H\u00fcllermeier","year":"2021","journal-title":"Mach. Learn."},{"key":"10.1016\/j.dsp.2026.106167_bib0050","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.dsp.2026.106167_bib0051","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","article-title":"A survey of uncertainty in deep neural networks","volume":"56","author":"Gawlikowski","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.dsp.2026.106167_bib0052","series-title":"KDD \u201923","article-title":"WinGNN","author":"Chen","year":"2023"},{"issue":"12","key":"10.1016\/j.dsp.2026.106167_bib0053","doi-asserted-by":"crossref","first-page":"195","DOI":"10.3390\/bdcc8120195","article-title":"Mandarin recognition based on self-attention DCNN-GRU","volume":"8","author":"Chen","year":"2024","journal-title":"Big Data Cogn. Comput."},{"key":"10.1016\/j.dsp.2026.106167_bib0054","article-title":"ResBiGAAT","volume":"107","author":"Abdelkader","year":"2023","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.dsp.2026.106167_bib0055","unstructured":"O. Soufan, S. Arafat, Arrhythmia Detection using Mutual Information-Based Integration Method, 2015. arXiv: 1502.01733."},{"issue":"1","key":"10.1016\/j.dsp.2026.106167_bib0056","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/1475-925X-8-31","article-title":"Robust arrhythmia classification using ELM","volume":"8","author":"Liu","year":"2009","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.dsp.2026.106167_bib0057","series-title":"BIOSTEC","article-title":"SVM With reject option for heartbeat classification","author":"Zidemal","year":"2009"},{"key":"10.1016\/j.dsp.2026.106167_bib0058","doi-asserted-by":"crossref","DOI":"10.1007\/s13246-025-01639-6","article-title":"ECG arrhythmia classification using DWT and attention CNN-BiGRU","author":"He","year":"2025","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.dsp.2026.106167_bib0059","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-75531-w","article-title":"Hybrid deep learning for 12-lead ECG arrhythmia diagnosis","volume":"14","author":"Bai","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.dsp.2026.106167_bib0060","doi-asserted-by":"crossref","first-page":"8804","DOI":"10.1038\/s41598-024-59311-0","article-title":"Multimodal ECG heartbeat classification with FCA-CNN","volume":"14","author":"Zhou","year":"2024","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.dsp.2026.106167_bib0061","first-page":"736","article-title":"ECG Classification based on CvT-13 and multimodal fusion","volume":"40","author":"Li","year":"2023","journal-title":"J. Biomed. Eng."},{"key":"10.1016\/j.dsp.2026.106167_bib0062","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s44163-025-00290-0","article-title":"FADLEC","volume":"5","author":"Lamba","year":"2025","journal-title":"Discov. Artif. Intell."},{"key":"10.1016\/j.dsp.2026.106167_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107516","article-title":"Efficient ECG classification using MODWPT and incremental BLS","volume":"104","author":"Li","year":"2025","journal-title":"Biomed. Signal Process. Contr."},{"key":"10.1016\/j.dsp.2026.106167_bib0064","article-title":"Imbalanced ECG data classification based on modified BLS","volume":"198","author":"Fan","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106167_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.110040","article-title":"Active broad learning system for ECG arrhythmia classification","volume":"185","author":"Fan","year":"2021","journal-title":"Measurement"}],"container-title":["Digital Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426002861?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426002861?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T15:59:25Z","timestamp":1783094365000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051200426002861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":65,"alternative-id":["S1051200426002861"],"URL":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106167","relation":{},"ISSN":["1051-2004"],"issn-type":[{"value":"1051-2004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adaptive gated temporal-aware multilayer broad learning system for ECG classification via uncertainty-driven transfer","name":"articletitle","label":"Article Title"},{"value":"Digital Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106167","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106167"}}