{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T13:18:29Z","timestamp":1783171109911,"version":"3.54.6"},"reference-count":45,"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\/501100003009","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["Z20251831020"],"award-info":[{"award-number":["Z20251831020"]}],"id":[{"id":"10.13039\/501100003009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016331","name":"Huanghuai University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016331","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["232102211038"],"award-info":[{"award-number":["232102211038"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["242102211029"],"award-info":[{"award-number":["242102211029"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["252102210044"],"award-info":[{"award-number":["252102210044"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["232102210129"],"award-info":[{"award-number":["232102210129"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["232102210076"],"award-info":[{"award-number":["232102210076"]}],"id":[{"id":"10.13039\/501100017700","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.2026.110412","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T03:06:42Z","timestamp":1777086402000},"page":"110412","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Self-organized channel attention network for myocardial infarction detection from single-lead ECG"],"prefix":"10.1016","volume":"122","author":[{"given":"Junming","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"zhiyi","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuankuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110412_b0005","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.jacc.2022.11.001","article-title":"Global burden of cardiovascular diseases and risks collaboration, 1990-2021","volume":"80","author":"Lindstrom","year":"2022","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.bspc.2026.110412_b0010","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1016\/j.amjmed.2022.08.017","article-title":"Mechanical complications of myocardial infarction","volume":"135","author":"Murphy","year":"2022","journal-title":"Am. J. Med."},{"key":"10.1016\/j.bspc.2026.110412_b0015","series-title":"Advanced Computing and Intelligent Technologies","first-page":"257","article-title":"A comparative study of myocardial infarction detection from ECG data using machine learning","author":"Chakraborty","year":"2022"},{"key":"10.1016\/j.bspc.2026.110412_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107187","article-title":"Deep learning in ECG diagnosis: A review","volume":"227","author":"Liu","year":"2021","journal-title":"Knowledge-Based Syst."},{"key":"10.1016\/j.bspc.2026.110412_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102462","article-title":"Atrial fibrillation detection with and without atrial activity analysis using lead-I mobile ECG technology","volume":"66","author":"Tuboly","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0030","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.asoc.2017.12.001","article-title":"Detection of myocardial infarction in 12 lead ECG using support vector machine","volume":"64","author":"Dohare","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.bspc.2026.110412_b0035","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1109\/TIM.2018.2816458","article-title":"Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data","volume":"67","author":"Sadhukhan","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.bspc.2026.110412_b0040","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s10916-016-0505-6","article-title":"Detection of cardiac abnormalities from multilead ECG using multiscale phase alternation features","volume":"40","author":"Tripathy","year":"2016","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.bspc.2026.110412_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104701","article-title":"Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG","volume":"83","author":"Chauhan","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0050","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2019.03.012","article-title":"Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features","volume":"175","author":"Han","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110412_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105766","article-title":"Automatic detection and localisation of myocardial infarction using multi-channel dense attention neural network","volume":"89","author":"Qiang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0060","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.knosys.2016.01.040","article-title":"Automated detection and localization of myocardial infarction using electrocardiogram: A comparative study of different leads","volume":"99","author":"Acharya","year":"2016","journal-title":"Knowledge-Based Syst."},{"key":"10.1016\/j.bspc.2026.110412_b0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3132833","article-title":"An efficient method for detection and localization of myocardial infarction","volume":"71","author":"Sahu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.bspc.2026.110412_b0070","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s11760-017-1146-z","article-title":"Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach","volume":"12","author":"Sharma","year":"2018","journal-title":"Signal, Image Video Process."},{"key":"10.1016\/j.bspc.2026.110412_b0075","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2016.10.013","article-title":"Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study","volume":"377","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"10.1016\/j.bspc.2026.110412_b0080","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cogsys.2019.09.001","article-title":"Application of multi-feature fusion and random forests to the automated detection of myocardial infarction","volume":"59","author":"Wang","year":"2020","journal-title":"Cognit Syst. Res."},{"key":"10.1016\/j.bspc.2026.110412_b0085","series-title":"Statistical Atlases and Computational Models of the Heart. M&ms and EMIDEC Challenges","first-page":"406","article-title":"Classification of pathological cases of myocardial infarction using convolutional neural network and random forest","author":"Shi","year":"2021"},{"key":"10.1016\/j.bspc.2026.110412_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104671","article-title":"Automatic diagnosis and localization of myocardial infarction using morphological features of ECG signal","volume":"83","author":"Ramezani Moghadam","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0095","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eswa.2018.12.037","article-title":"A robust deep convolutional neural network with batch-weighted loss for heartbeat classification","volume":"122","author":"Sellami","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110412_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106582","article-title":"Inter-patient arrhythmia classification with improved deep residual convolutional neural network","volume":"214","author":"Li","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110412_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103663","article-title":"A novel bidirectional LSTM network based on scale factor for atrial fibrillation signals classification","volume":"76","author":"Feng","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105789","article-title":"Inter-patient ECG arrhythmia heartbeat classification network based on multiscale convolution and FCBA","volume":"90","author":"Zhou","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0115","first-page":"22","article-title":"Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram, Biomed. Signal Process","volume":"45","author":"Liu","year":"2018","journal-title":"Control"},{"key":"10.1016\/j.bspc.2026.110412_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106199","article-title":"MCA-net: A multi-task channel attention network for myocardial infarction detection and location using 12-lead ECGs","volume":"150","author":"Pan","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110412_b0125","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.ins.2022.05.070","article-title":"A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning","volume":"606","author":"He","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.bspc.2026.110412_b0130","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.tcm.2019.10.010","article-title":"How useful is the smartwatch ECG?","volume":"30","author":"Isakadze","year":"2020","journal-title":"Trends Cardiovasc. Med."},{"key":"10.1016\/j.bspc.2026.110412_b0135","article-title":"Diagnostic accuracy of smartwatches for the detection of cardiac arrhythmia: Systematic review and meta-analysis","volume":"23","author":"Nazarian","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.bspc.2026.110412_b0140","doi-asserted-by":"crossref","first-page":"7246","DOI":"10.3390\/s20247246","article-title":"Automatic classification of myocardial infarction using spline representation of single-lead derived vectorcardiography","volume":"20","author":"Chuang","year":"2020","journal-title":"Sens"},{"key":"10.1016\/j.bspc.2026.110412_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102683","article-title":"Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using long short-term memory neural network","volume":"68","author":"Martin","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110412_b0150","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2022.783184","article-title":"Detection and localization of myocardial infarction based on multi-scale ResNet and attention mechanism","volume":"13","author":"Cao","year":"2022","journal-title":"Front. Physiol."},{"key":"10.1016\/j.bspc.2026.110412_b0155","first-page":"1","article-title":"Mobi-trans: A hybrid network with attention mechanism for myocardial infarction localization, in","volume":"2022","author":"Shan","year":"2022","journal-title":"Int. Joint Conference on Neural Networks (IJCNN)"},{"key":"10.1016\/j.bspc.2026.110412_b0160","series-title":"In: 2020 IEEE 17th India Council International Conference (INDICON)","first-page":"1","article-title":"An attention based hierarchical LSTM model for detection of myocardial infarction","author":"Jyotishi","year":"2020"},{"key":"10.1016\/j.bspc.2026.110412_b0165","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"10.1016\/j.bspc.2026.110412_b0170","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.110412_b0175","first-page":"463","article-title":"A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches, IEEE transactions on Systems, man, and Cybernetics","volume":"42","author":"Galar","year":"2012","journal-title":"Part C (applications and Reviews)."},{"key":"10.1016\/j.bspc.2026.110412_b0180","first-page":"7132","article-title":"Squeeze-and-excitation networks, in","volume":"2018","author":"Hu","year":"2018","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"10.1016\/j.bspc.2026.110412_b0185","series-title":"Computer Vision \u2013 ECCV 2018","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.bspc.2026.110412_b0190","doi-asserted-by":"crossref","unstructured":"X. Wang, R. Girshick, A. Gupta, K. He, Non-local neural networks, in: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, UT, USA, 2018: pp. 7794\u20137803.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"10.1016\/j.bspc.2026.110412_b0195","series-title":"8th European Medical and Biological Engineering Conference","first-page":"341","article-title":"Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms","author":"Gupta","year":"2021"},{"key":"10.1016\/j.bspc.2026.110412_b0200","first-page":"520","article-title":"Detection of myocardial infarction and arrhythmia from single-lead ECG data using bagging trees classifier, in","volume":"2017","author":"Khatun","year":"2017","journal-title":"IEEE Int. Conference on Electro Inform. Technol. (EIT)"},{"key":"10.1016\/j.bspc.2026.110412_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118398","article-title":"Automated localization and severity period prediction of myocardial infarction with clinical interpretability based on deep learning and knowledge graph","volume":"209","author":"Han","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110412_b0210","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TBCAS.2018.2848477","article-title":"Real-time event-driven classification technique for early detection and prevention of myocardial infarction on wearable systems","volume":"12","author":"Sopic","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"10.1016\/j.bspc.2026.110412_b0215","doi-asserted-by":"crossref","first-page":"15001","DOI":"10.1088\/1361-6579\/aaf34d","article-title":"Detecting and interpreting myocardial infarction using fully convolutional neural networks","volume":"40","author":"Strodthoff","year":"2019","journal-title":"Physiol. Meas."},{"key":"10.1016\/j.bspc.2026.110412_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105138","article-title":"ML\u2013ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG","volume":"185","author":"Han","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110412_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106762","article-title":"A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification","volume":"219","author":"Fang","year":"2022","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009663?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009663?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:36:47Z","timestamp":1783168607000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":45,"alternative-id":["S1746809426009663"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110412","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":"Self-organized channel attention network for myocardial infarction detection from single-lead ECG","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110412","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":"110412"}}