{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:01:41Z","timestamp":1784530901649,"version":"3.55.0"},"reference-count":114,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001794","name":"The University of Queensland","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001794","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The 12-lead electrocardiogram (ECG) is the clinical gold standard for cardiovascular disease (CVD) detection, though its use in self-care and resource-limited settings is constrained by complex configurations and reliance on specialised diagnostic infrastructure. Commercially available portable\/wearable artificial intelligence-enabled ECGs (AI-ECGs) using fewer leads offer greater accessibility yet face clinical integration challenges due to misalignment with gold standard lead configurations, and restricted detection capabilities and clinical interpretability. Consequently, recent research emphasises development of clinically aligned fewer-lead AI-ECG approaches, where the leads are aligned with the 12-lead ECG and used individually or in combinations. However, these approaches remain largely pre-commercial, and comprehensive evaluation is needed to determine their potential and limitations for clinical and self-care acceptability. This review critically assesses 316 studies in terms of detected CVD types, selected leads and rationale for lead selection, and diagnostic performance of AI methods. Key findings reveal limitations in detectable CVD types, diagnostic consistency, incorporation of cardiac electrophysiology and lead interdependency insights, along with the limited adoption of advanced multi-task learning, adaptive methods and explainable AI models. This review also outlines future directions to develop compact and clinically aligned fewer-lead AI-ECGs, bridging the gap between innovation and practical application in clinical and self-care settings.<\/jats:p>","DOI":"10.1007\/s10462-026-11577-8","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:03:59Z","timestamp":1778252639000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-enabled clinically aligned fewer-lead ECG approaches for detecting cardiovascular diseases"],"prefix":"10.1007","volume":"59","author":[{"given":"Tasriva","family":"Sikandar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Tariq","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-2214","authenticated-orcid":false,"given":"AKM","family":"Azad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asaduzzaman","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"11577_CR1","doi-asserted-by":"publisher","first-page":"167605","DOI":"10.1109\/access.2019.2953920","volume":"7","author":"M Abdar","year":"2019","unstructured":"Abdar M, Acharya UR, Sarrafzadegan N, Makarenkov V (2019) NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access 7:167605\u2013167620. https:\/\/doi.org\/10.1109\/access.2019.2953920","journal-title":"IEEE Access"},{"key":"11577_CR2","doi-asserted-by":"crossref","unstructured":"Abdullah TAA et al (2024) Sig-LIME: a signal-based enhancement of LIME explanation technique. IEEE Access","DOI":"10.1109\/ACCESS.2024.3384277"},{"key":"11577_CR3","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.cmpb.2018.04.018","volume":"161","author":"M Adam","year":"2018","unstructured":"Adam M et al (2018) Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals. Comput Methods Programs Biomed 161:133\u2013143. https:\/\/doi.org\/10.1016\/j.cmpb.2018.04.018","journal-title":"Comput Methods Programs Biomed"},{"key":"11577_CR4","unstructured":"Australian Institute of Health and Welfare (AIHW) (2024) Heart, stroke and vascular disease: Australian facts. [Online]. Available: https:\/\/www.aihw.gov.au\/reports\/heart-stroke-vascular-diseases\/hsvd-facts\/contents\/disease-types. Accessed: 28 June 2024"},{"key":"11577_CR5","unstructured":"AliveCor, Inc. (2025) Instructions for use (IFU) for the KardiaMobile 6L System (AC-019). Accessed: 30 December 2025"},{"issue":"4","key":"11577_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3501813","volume":"13","author":"RS Antunes","year":"2022","unstructured":"Antunes RS, Andr\u00e9 da Costa C, K\u00fcderle A, Yari IA, Eskofier B (2022) Federated learning for healthcare: systematic review and architecture proposal. ACM Trans Intell Syst Technol 13(4):1\u201323","journal-title":"ACM Trans Intell Syst Technol"},{"key":"11577_CR7","unstructured":"\"ECG app and irregular rhythm notification now available on Apple Watch.\" https:\/\/www.apple.com\/au\/newsroom\/2021\/05\/ecg-app-and-irregular-rhythm-notification-now-available-on-apple-watch\/. Accessed 30 December 2025."},{"key":"11577_CR8","unstructured":"Take an ECG with the ECG app on Apple Watch. https:\/\/support.apple.com\/en-au\/120278. Accessed 30 December 2025."},{"issue":"3","key":"11577_CR9","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/TITB.2010.2047754","volume":"14","author":"H Atoui","year":"2010","unstructured":"Atoui H, Fayn J, Rubel P (2010) A novel neural-network model for deriving standard 12-lead ECGs from serial three-lead ECGs: application to self-care. IEEE Trans Inf Technol Biomed 14(3):883\u2013890","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"11577_CR10","unstructured":"Australian Bureau of Statistics (2023) Heart, stroke and vascular disease. Accessed: 2 February 2024. [Online]. Available: https:\/\/www.abs.gov.au\/statistics\/health\/health-conditions-and-risks\/heart-stroke-and-vascular-disease\/latest-release"},{"issue":"2","key":"11577_CR11","doi-asserted-by":"publisher","first-page":"e117","DOI":"10.1016\/S2589-7500(21)00256-9","volume":"4","author":"P Bachtiger","year":"2022","unstructured":"Bachtiger P et al (2022) Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. Lancet Digit Health 4(2):e117\u2013e125. https:\/\/doi.org\/10.1016\/S2589-7500(21)00256-9","journal-title":"Lancet Digit Health"},{"key":"11577_CR12","volume-title":"Braunwald's heart disease e-book: a textbook of cardiovascular medicine","author":"RO Bonow","year":"2011","unstructured":"Bonow RO, Mann DL, Zipes DP, Libby P (2011) Braunwald\u2019s heart disease e-book: a textbook of cardiovascular medicine. Elsevier Health Sciences, Amsterdam"},{"key":"11577_CR13","doi-asserted-by":"crossref","unstructured":"Bradfield EP et al (2025) Understanding cardiac anatomy and imaging to improve safety of procedures: 12-lead electrocardiographic anatomy. JACC: case reports, pp 105998","DOI":"10.1016\/j.jaccas.2025.105998"},{"issue":"4","key":"11577_CR14","doi-asserted-by":"publisher","first-page":"184","DOI":"10.23838\/pfm.2021.00058","volume":"5","author":"E Caires Silveira","year":"2021","unstructured":"Caires Silveira E (2021) Automated atrial fibrillation recognition in 12-lead electrocardiographic records: a signal to image and transfer learning approach: a case-control accuracy study. Precis Future Med 5(4):184\u2013189. https:\/\/doi.org\/10.23838\/pfm.2021.00058","journal-title":"Precis Future Med"},{"key":"11577_CR15","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2023.2270101","author":"MK Chaitanya","year":"2023","unstructured":"Chaitanya MK, Sharma LD (2023) Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier. Comput Methods Biomech Biomed Eng. https:\/\/doi.org\/10.1080\/10255842.2023.2270101","journal-title":"Comput Methods Biomech Biomed Eng"},{"issue":"10","key":"11577_CR16","doi-asserted-by":"publisher","first-page":"3165","DOI":"10.1016\/j.asoc.2012.06.004","volume":"12","author":"P-C Chang","year":"2012","unstructured":"Chang P-C, Lin J-J, Hsieh J-C, Weng J (2012) Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl Soft Comput 12(10):3165\u20133175. https:\/\/doi.org\/10.1016\/j.asoc.2012.06.004","journal-title":"Appl Soft Comput"},{"key":"11577_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104701","author":"C Chauhan","year":"2023","unstructured":"Chauhan C, Tripathy RK, Agrawal M (2023) Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2023.104701","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"11577_CR18","doi-asserted-by":"publisher","first-page":"20495","DOI":"10.1038\/s41598-020-77599-6","volume":"10","author":"Y Cho","year":"2020","unstructured":"Cho Y et al (2020) Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography. Sci Rep 10(1):20495. https:\/\/doi.org\/10.1038\/s41598-020-77599-6","journal-title":"Sci Rep"},{"key":"11577_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12030336","author":"HY Choi","year":"2022","unstructured":"Choi HY et al (2022) Diagnostic accuracy of the deep learning model for the detection of ST elevation myocardial infarction on electrocardiogram. J Pers Med. https:\/\/doi.org\/10.3390\/jpm12030336","journal-title":"J Pers Med"},{"key":"11577_CR20","doi-asserted-by":"publisher","DOI":"10.1101\/2024.11.14.24317328","author":"E Coppola","year":"2024","unstructured":"Coppola E, Savardi M, Massussi M, Adamo M, Metra M, Signoroni A (2024) HuBERT-ECG as a self-supervised foundation model for broad and scalable cardiac applications. medRxiv. https:\/\/doi.org\/10.1101\/2024.11.14.24317328","journal-title":"medRxiv"},{"key":"11577_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106035","volume":"203","author":"H Dai","year":"2021","unstructured":"Dai H, Hwang HG, Tseng VS (2021) Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals. Comput Methods Programs Biomed 203:106035. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106035","journal-title":"Comput Methods Programs Biomed"},{"key":"11577_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101997","author":"Y Deng","year":"2020","unstructured":"Deng Y et al (2020) ST-Net: synthetic ECG tracings for diagnosing various cardiovascular diseases. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2020.101997","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"11577_CR23","first-page":"29","volume":"19","author":"L Desteghe","year":"2017","unstructured":"Desteghe L et al (2017) Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting. Ep Europace 19(1):29\u201339","journal-title":"Ep Europace"},{"issue":"6","key":"11577_CR24","doi-asserted-by":"publisher","first-page":"5261","DOI":"10.1007\/s10462-022-10304-3","volume":"56","author":"F Di Martino","year":"2023","unstructured":"Di Martino F, Delmastro F (2023) Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artific Intell Rev 56(6):5261\u20135315","journal-title":"Artific Intell Rev"},{"issue":"1","key":"11577_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-02228-3","volume":"9","author":"Z Ding","year":"2025","unstructured":"Ding Z et al (2025) AI modeling photoplethysmography to electrocardiography useful for predicting cardiovascular disease. NPJ Digit Med 9(1):61. https:\/\/doi.org\/10.1038\/s41746-025-02228-3","journal-title":"NPJ Digit Med"},{"issue":"2","key":"11577_CR26","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.jacep.2018.10.006","volume":"5","author":"M D\u00f6rr","year":"2019","unstructured":"D\u00f6rr M et al (2019) The WATCH AF trial: SmartWATCHes for detection of atrial fibrillation. JACC Clin Electrophysiol 5(2):199\u2013208","journal-title":"JACC Clin Electrophysiol"},{"key":"11577_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120107","author":"LT Duong","year":"2023","unstructured":"Duong LT, Doan TTH, Chu CQ, Nguyen PT (2023) Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.120107","journal-title":"Expert Syst Appl"},{"key":"11577_CR28","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.jelectrocard.2022.12.001","volume":"77","author":"T Dwivedi","year":"2023","unstructured":"Dwivedi T, Xue J, Treiman D, Dubey A, Albert D (2023) Machine learning models of 6-lead ECGs for the interpretation of left ventricular hypertrophy (LVH). J Electrocardiol 77:62\u201367. https:\/\/doi.org\/10.1016\/j.jelectrocard.2022.12.001","journal-title":"J Electrocardiol"},{"issue":"1","key":"11577_CR29","doi-asserted-by":"publisher","first-page":"14","DOI":"10.2174\/157340312801215782","volume":"8","author":"M Elgendi","year":"2012","unstructured":"Elgendi M (2012) On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 8(1):14\u201325","journal-title":"Curr Cardiol Rev"},{"issue":"24","key":"11577_CR30","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.1016\/j.jacc.2024.03.400","volume":"83","author":"P Elias","year":"2024","unstructured":"Elias P et al (2024) Artificial intelligence for cardiovascular care\u2014part 1: advances: JACC review topic of the week. J Am College Cardiol 83(24):2472\u20132486","journal-title":"J Am College Cardiol"},{"key":"11577_CR31","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2021.754321","volume":"8","author":"Z-y Fan","year":"2021","unstructured":"Fan Z-y, Yang Y, Yin R-y, Tang L, Zhang F (2021) Effect of health literacy on decision delay in patients with acute myocardial infarction. Front Cardiovasc Med 8:754321","journal-title":"Front Cardiovasc Med"},{"key":"11577_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11081446","author":"O Faust","year":"2021","unstructured":"Faust O, Kareem M, Ali A, Ciaccio EJ, Acharya UR (2021) Automated arrhythmia detection based on RR intervals. Diagnostics (Basel). https:\/\/doi.org\/10.3390\/diagnostics11081446","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"11577_CR33","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MPULS.2018.2885832","volume":"10","author":"KR Foster","year":"2019","unstructured":"Foster KR, Torous J (2019) The opportunity and obstacles for smartwatches and wearable sensors. IEEE Pulse 10(1):22\u201325","journal-title":"IEEE Pulse"},{"key":"11577_CR34","volume-title":"Introduction to 12-lead ECG: the art of interpretation","author":"TB Garcia","year":"2014","unstructured":"Garcia TB (2014) Introduction to 12-lead ECG: the art of interpretation. Jones & Bartlett Publishers, Burlington"},{"key":"11577_CR35","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ijcard.2021.11.039","volume":"346","author":"CM Gibson","year":"2022","unstructured":"Gibson CM et al (2022) Evolution of single-lead ECG for STEMI detection using a deep learning approach. Int J Cardiol 346:47\u201352. https:\/\/doi.org\/10.1016\/j.ijcard.2021.11.039","journal-title":"Int J Cardiol"},{"key":"11577_CR36","doi-asserted-by":"publisher","first-page":"98909","DOI":"10.1109\/ACCESS.2022.3206449","volume":"10","author":"T Gon\u00e7alves","year":"2022","unstructured":"Gon\u00e7alves T, Rio-Torto I, Teixeira LF, Cardoso JS (2022) A survey on attention mechanisms for medical applications: are we moving toward better algorithms? IEEE Access 10:98909\u201398935","journal-title":"IEEE Access"},{"issue":"3","key":"11577_CR37","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan H, Liu M (2021) Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng 69(3):1173\u20131185","journal-title":"IEEE Trans Biomed Eng"},{"key":"11577_CR38","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ajem.2024.07.043","volume":"84","author":"S Gunay","year":"2024","unstructured":"Gunay S, Ozturk A, Yigit Y (2024) The accuracy of Gemini, GPT-4, and GPT-4o in ECG analysis: a comparison with cardiologists and emergency medicine specialists. Am J Emerg Med 84:68\u201373. https:\/\/doi.org\/10.1016\/j.ajem.2024.07.043","journal-title":"Am J Emerg Med"},{"issue":"1","key":"11577_CR39","doi-asserted-by":"publisher","DOI":"10.2196\/58202","volume":"13","author":"A Guni","year":"2024","unstructured":"Guni A, Sounderajah V, Whiting P, Bossuyt P, Darzi A, Ashrafian H (2024) Revised tool for the quality assessment of diagnostic accuracy studies using AI (QUADAS-AI): protocol for a qualitative study. JMIR Res Protoc 13(1):e58202","journal-title":"JMIR Res Protoc"},{"issue":"1","key":"11577_CR40","doi-asserted-by":"publisher","first-page":"19615","DOI":"10.1038\/s41598-022-24254-x","volume":"12","author":"S Gustafsson","year":"2022","unstructured":"Gustafsson S et al (2022) Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients. Sci Rep 12(1):19615. https:\/\/doi.org\/10.1038\/s41598-022-24254-x","journal-title":"Sci Rep"},{"key":"11577_CR41","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.cmpb.2019.03.012","volume":"175","author":"C Han","year":"2019","unstructured":"Han C, Shi L (2019) Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput Methods Programs Biomed 175:9\u201323. https:\/\/doi.org\/10.1016\/j.cmpb.2019.03.012","journal-title":"Comput Methods Programs Biomed"},{"key":"11577_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118398","author":"C Han","year":"2022","unstructured":"Han C, Pan S, Que W, Wang Z, Zhai Y, Shi L (2022) Automated localization and severity period prediction of myocardial infarction with clinical interpretability based on deep learning and knowledge graph. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2022.118398","journal-title":"Expert Syst Appl"},{"issue":"5","key":"11577_CR43","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.annemergmed.2004.09.012","volume":"46","author":"RF Harrison","year":"2005","unstructured":"Harrison RF, Kennedy RL (2005) Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med 46(5):431\u2013439. https:\/\/doi.org\/10.1016\/j.annemergmed.2004.09.012","journal-title":"Ann Emerg Med"},{"key":"11577_CR44","unstructured":"HealthTech Guidance (2023) Kardia mobile for detecting atrial fibrillation (HTG606). National Institute for Health and Care Excellence (NICE). Accessed: 30 December 2025. [Online]. Available: https:\/\/www.nice.org.uk\/guidance\/htg606\/resources\/kardiamobile-for-detecting-atrial-fibrillation-pdf-1809598348031173"},{"key":"11577_CR45","unstructured":"Heart Foundation (2024) Key statistics: cardiovascular disease. [Online]. Available: https:\/\/wwwAccessed: 26 June 2024"},{"key":"11577_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101700","author":"J Heo","year":"2020","unstructured":"Heo J, Lee JJ, Kwon S, Kim B, Hwang SO, Yoon YR (2020) A novel method for detecting ST segment elevation myocardial infarction on a 12-lead electrocardiogram with a three-dimensional display. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2019.101700","journal-title":"Biomed Signal Process Control"},{"key":"11577_CR47","doi-asserted-by":"crossref","unstructured":"Hernandez AA, Bonizzi P, Karel J, Peeters R (2018) Myocardial ischemia diagnosis using a reduced lead system. IEEE, pp 5302\u20135305","DOI":"10.1109\/EMBC.2018.8513511"},{"key":"11577_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2023.101211","volume":"46","author":"N Hirota","year":"2023","unstructured":"Hirota N et al (2023) Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms. IJC Heart Vasc 46:101211. https:\/\/doi.org\/10.1016\/j.ijcha.2023.101211","journal-title":"IJC Heart Vasc"},{"issue":"6","key":"11577_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2025.100948","volume":"6","author":"J Huang","year":"2025","unstructured":"Huang J et al (2025) Foundation models and intelligent decision-making: progress, challenges, and perspectives. Innovation 6(6):100948. https:\/\/doi.org\/10.1016\/j.xinn.2025.100948","journal-title":"Innovation"},{"issue":"1","key":"11577_CR50","doi-asserted-by":"publisher","DOI":"10.2196\/62862","volume":"13","author":"L Hunik","year":"2025","unstructured":"Hunik L et al (2025) Diagnostic prediction models for primary care, based on AI and electronic health records: systematic review. JMIR Med Inform 13(1):e62862","journal-title":"JMIR Med Inform"},{"key":"11577_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104457","volume":"134","author":"V Jahmunah","year":"2021","unstructured":"Jahmunah V, Ng EYK, San TR, Acharya UR (2021) Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput Biol Med 134:104457. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104457","journal-title":"Comput Biol Med"},{"issue":"1","key":"11577_CR52","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s41746-021-00544-y","volume":"5","author":"S Jayakumar","year":"2022","unstructured":"Jayakumar S et al (2022) Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. Npj Digit Med 5(1):11 (%@ 2398-6352)","journal-title":"Npj Digit Med"},{"key":"11577_CR53","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ac8469","author":"M Jiang","year":"2022","unstructured":"Jiang M et al (2022) Visualization deep learning model for automatic arrhythmias classification. Physiol Meas. https:\/\/doi.org\/10.1088\/1361-6579\/ac8469","journal-title":"Physiol Meas"},{"issue":"3","key":"11577_CR54","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s40138-022-00248-x","volume":"10","author":"P Kamga","year":"2022","unstructured":"Kamga P, Mostafa R, Zafar S (2022) The use of wearable ECG devices in the clinical setting: a review. Curr Emerg Hosp Med Rep 10(3):67\u201372","journal-title":"Curr Emerg Hosp Med Rep"},{"issue":"1","key":"11577_CR55","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00869-w","volume":"6","author":"A Khunte","year":"2023","unstructured":"Khunte A et al (2023) Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. NPJ Digit Med 6(1):124. https:\/\/doi.org\/10.1038\/s41746-023-00869-w","journal-title":"NPJ Digit Med"},{"issue":"1","key":"11577_CR56","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-024-00492-0","volume":"4","author":"FR Kolbinger","year":"2024","unstructured":"Kolbinger FR, Veldhuizen GP, Zhu J, Truhn D, Kather JN (2024) Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis. Commun Med 4(1):71","journal-title":"Commun Med"},{"issue":"2","key":"11577_CR57","doi-asserted-by":"publisher","first-page":"70","DOI":"10.32725\/jab.2022.008","volume":"20","author":"R Kumar","year":"2022","unstructured":"Kumar R, Aggarwal Y, Nigam VK (2022) Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine. J Appl Biomed 20(2):70\u201379. https:\/\/doi.org\/10.32725\/jab.2022.008","journal-title":"J Appl Biomed"},{"issue":"2212","key":"11577_CR58","doi-asserted-by":"publisher","first-page":"20200258","DOI":"10.1098\/rsta.2020.0258","volume":"379","author":"C Lai","year":"2021","unstructured":"Lai C, Zhou S, Trayanova NA (2021) Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification. Philos Trans R Soc A Math Phys Eng Sci 379(2212):20200258","journal-title":"Philos Trans R Soc A Math Phys Eng Sci"},{"key":"11577_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104963","author":"KH Le","year":"2023","unstructured":"Le KH, Pham HH, Nguyen TBT, Nguyen TA, Thanh TN, Do CD (2023) LightX3ECG: A lightweight and eXplainable deep learning system for 3-lead electrocardiogram classification. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2023.104963","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"11577_CR60","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1109\/JBHI.2019.2936583","volume":"24","author":"J Lee","year":"2020","unstructured":"Lee J, Oh K, Kim B, Yoo SK (2020) Synthesis of electrocardiogram V-lead signals from limb-lead measurement using R-peak aligned generative adversarial network. IEEE J Biomed Health Inform 24(5):1265\u20131275. https:\/\/doi.org\/10.1109\/JBHI.2019.2936583","journal-title":"IEEE J Biomed Health Inform"},{"key":"11577_CR61","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/BF02442844","volume":"25","author":"CL Levkov","year":"1987","unstructured":"Levkov CL (1987) Orthogonal electrocardiogram derived from the limb and chest electrodes of the conventional 12-lead system. Med Biol Eng Comput 25:155\u2013164","journal-title":"Med Biol Eng Comput"},{"key":"11577_CR62","doi-asserted-by":"publisher","DOI":"10.3390\/sym12122019","author":"D Li","year":"2020","unstructured":"Li D, Tao Y, Zhao J, Wu H (2020a) Classification of congestive heart failure from ECG segments with a multi-scale residual network. Symmetry (Basel). https:\/\/doi.org\/10.3390\/sym12122019","journal-title":"Symmetry (Basel)"},{"key":"11577_CR63","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.trb.2020.06.009","volume":"139","author":"Z-C Li","year":"2020","unstructured":"Li Z-C, Huang H-J, Yang H (2020b) Fifty years of the bottleneck model: A bibliometric review and future research directions. Transp. Res. Part b: Methodol. 139:311\u2013342","journal-title":"Transp. Res. Part b: Methodol."},{"key":"11577_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.114137","author":"Z Li","year":"2025","unstructured":"Li Z et al (2025) An early warning method for arrhythmias in long-term ECGs based on self-supervised learning and LSTM. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2025.114137","journal-title":"Knowl-Based Syst"},{"key":"11577_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101789","volume":"103","author":"OS Lih","year":"2020","unstructured":"Lih OS et al (2020) Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med 103:101789. https:\/\/doi.org\/10.1016\/j.artmed.2019.101789","journal-title":"Artif Intell Med"},{"key":"11577_CR66","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12071150","author":"YL Liu","year":"2022","unstructured":"Liu YL, Lin CS, Cheng CC, Lin C (2022) A deep learning algorithm for detecting acute pericarditis by electrocardiogram. J Pers Med. https:\/\/doi.org\/10.3390\/jpm12071150","journal-title":"J Pers Med"},{"key":"11577_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103753","author":"C Ma","year":"2022","unstructured":"Ma C, Lan K, Wang J, Yang Z, Zhang Z (2022) Arrhythmia detection based on multi-scale fusion of hybrid deep models from single lead ECG recordings: a multicenter dataset study. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2022.103753","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"11577_CR68","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.jelectrocard.2015.12.008","volume":"49","author":"S Maheshwari","year":"2016","unstructured":"Maheshwari S, Acharyya A, Schiariti M, Puddu PE (2016) Frank vectorcardiographic system from standard 12 lead ECG: an effort to enhance cardiovascular diagnosis. J Electrocardiol 49(2):231\u2013242","journal-title":"J Electrocardiol"},{"key":"11577_CR69","doi-asserted-by":"publisher","first-page":"106586","DOI":"10.1016\/j.asoc.2020.106586","volume":"96","author":"RA Mahmoud","year":"2020","unstructured":"Mahmoud RA, Hajj H, Karameh FN (2020) A systematic approach to multi-task learning from time-series data. Appl Soft Comput 96:106586","journal-title":"Appl Soft Comput"},{"key":"11577_CR70","doi-asserted-by":"crossref","unstructured":"Malmivuo J, Plonsey R (1975) Bioelectromagnetism. 15. 12-lead ECG system. Bioelectromag-Principles Appl Bioelectric Biomagnetis Fields, pp 277\u2013289","DOI":"10.1093\/acprof:oso\/9780195058239.003.0015"},{"issue":"1","key":"11577_CR71","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1038\/s41746-024-01193-7","volume":"7","author":"F Mason","year":"2024","unstructured":"Mason F, Pandey AC, Gadaleta M, Topol EJ, Muse ED, Quer G (2024) AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment. NPJ Digital Med 7(1):201","journal-title":"NPJ Digital Med"},{"issue":"7334","key":"11577_CR72","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1136\/bmj.324.7334.415","volume":"324","author":"S Meek","year":"2002","unstructured":"Meek S, Morris F (2002) Introduction. I\u2014leads, rate, rhythm, and cardiac axis. BMJ 324(7334):415\u2013418","journal-title":"BMJ"},{"key":"11577_CR73","doi-asserted-by":"publisher","first-page":"106345","DOI":"10.1016\/j.neunet.2024.106345","volume":"176","author":"H Meng","year":"2024","unstructured":"Meng H, Wagner C, Triguero I (2024) SEGAL time series classification\u2014stable explanations using a generative model and an adaptive weighting method for LIME. Neural Netw 176:106345","journal-title":"Neural Netw"},{"key":"11577_CR74","doi-asserted-by":"publisher","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","volume":"7","author":"S Mohan","year":"2019","unstructured":"Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542\u201381554","journal-title":"IEEE Access"},{"key":"11577_CR75","unstructured":"Monachino G et al. (2025) Self-DANA: a resource-efficient channel-adaptive self-supervised approach for ECG foundation models. arXiv preprint arXiv:2507.14151"},{"issue":"1","key":"11577_CR76","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0226990","volume":"15","author":"S Mousavi","year":"2020","unstructured":"Mousavi S, Fotoohinasab A, Afghah F (2020) Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks. PLoS ONE 15(1):e0226990. https:\/\/doi.org\/10.1371\/journal.pone.0226990","journal-title":"PLoS ONE"},{"issue":"1","key":"11577_CR77","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.eswa.2012.07.032","volume":"40","author":"J Nahar","year":"2013","unstructured":"Nahar J, Imam T, Tickle KS, Chen Y-PP (2013) Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst Appl 40(1):96\u2013104","journal-title":"Expert Syst Appl"},{"key":"11577_CR78","doi-asserted-by":"publisher","DOI":"10.1101\/2025.03.02.25322575","author":"A Nolin-Lapalme","year":"2025","unstructured":"Nolin-Lapalme A et al (2025) Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised electrocardiogram foundation models. medRxiv. https:\/\/doi.org\/10.1101\/2025.03.02.25322575","journal-title":"medRxiv"},{"key":"11577_CR79","unstructured":"Oh J, Chung H, Kwon J-m, Hong D-g, Choi E (2022) Lead-agnostic self-supervised learning for local and global representations of electrocardiogram. PMLR, pp 338\u2013353."},{"issue":"4","key":"11577_CR80","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1046\/j.1475-097x.2002.00433.x","volume":"22","author":"SE Olsson","year":"2002","unstructured":"Olsson SE, Ohlsson M, Ohlin H, Edenbrandt L (2002) Neural networks--a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block. Clin Physiol Funct Imaging 22(4):295\u2013299. https:\/\/doi.org\/10.1046\/j.1475-097x.2002.00433.x","journal-title":"Clin Physiol Funct Imaging"},{"key":"11577_CR81","first-page":"1756","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:1756\u20131833","journal-title":"BMJ"},{"key":"11577_CR82","doi-asserted-by":"publisher","DOI":"10.3390\/app10186495","author":"J Park","year":"2020","unstructured":"Park J, Kim J-k, Jung S, Gil Y, Choi J-I, Son HS (2020) ECG-Signal multi-classification model based on Squeeze-and-Excitation residual neural networks. Appl Sci (Basel). https:\/\/doi.org\/10.3390\/app10186495","journal-title":"Appl Sci (Basel)"},{"issue":"6","key":"11577_CR83","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1109\/JBHI.2015.2478076","volume":"20","author":"O Perlman","year":"2016","unstructured":"Perlman O, Katz A, Amit G, Zigel Y (2016) Supraventricular tachycardia classification in the 12-Lead ECG using atrial waves detection and a clinically based tree scheme. IEEE J Biomed Health Inform 20(6):1513\u20131520. https:\/\/doi.org\/10.1109\/JBHI.2015.2478076","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"11577_CR84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3412357","volume":"21","author":"B Pfitzner","year":"2021","unstructured":"Pfitzner B, Steckhan N, Arnrich B (2021) Federated learning in a medical context: a systematic literature review. ACM Trans Internet Technol 21(2):1\u201331","journal-title":"ACM Trans Internet Technol"},{"key":"11577_CR85","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1109\/lsp.2020.3036314","volume":"27","author":"E Prabhakararao","year":"2020","unstructured":"Prabhakararao E, Dandapat S (2020) Attentive RNN-based network to fuse 12-Lead ECG and clinical features for improved myocardial infarction diagnosis. IEEE Signal Process Lett 27:2029\u20132033. https:\/\/doi.org\/10.1109\/lsp.2020.3036314","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"11577_CR86","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15432-4","volume":"11","author":"AH Ribeiro","year":"2020","unstructured":"Ribeiro AH et al (2020) Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 11(1):1760. https:\/\/doi.org\/10.1038\/s41467-020-15432-4","journal-title":"Nat Commun"},{"key":"11577_CR87","doi-asserted-by":"publisher","first-page":"41758","DOI":"10.1109\/ACCESS.2022.3167702","volume":"10","author":"Y Sawada","year":"2022","unstructured":"Sawada Y, Nakamura K (2022) Concept bottleneck model with additional unsupervised concepts. IEEE Access 10:41758\u201341765","journal-title":"IEEE Access"},{"issue":"13","key":"11577_CR88","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1093\/eurheartj\/ehy004","volume":"39","author":"KH Scholz","year":"2018","unstructured":"Scholz KH et al (2018) Impact of treatment delay on mortality in ST-segment elevation myocardial infarction (STEMI) patients presenting with and without haemodynamic instability: results from the German prospective, multicentre FITT-STEMI trial. Eur Heart J 39(13):1065\u20131074","journal-title":"Eur Heart J"},{"issue":"4","key":"11577_CR89","doi-asserted-by":"publisher","first-page":"327","DOI":"10.2459\/JCM.0000000000001131","volume":"22","author":"T Scquizzato","year":"2021","unstructured":"Scquizzato T, Frontera A, Limite LR, Landoni G (2021) Smartwatch-detected atrial fibrillation in the emergency department: possible implications and treatment. J Cardiovasc Med 22(4):327\u2013328","journal-title":"J Cardiovasc Med"},{"key":"11577_CR90","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6455053","volume":"2021","author":"MB Shahnawaz","year":"2021","unstructured":"Shahnawaz MB, Dawood H, Lo Schiavo A (2021) An effective deep learning model for automated detection of myocardial infarction based on ultrashort-term heart rate variability analysis. Math Probl Eng 2021:1\u201313. https:\/\/doi.org\/10.1155\/2021\/6455053","journal-title":"Math Probl Eng"},{"key":"11577_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2023.100093","author":"MA Sheikh Beig Goharrizi","year":"2023","unstructured":"Sheikh Beig Goharrizi MA, Teimourpour A, Falah M, Hushmandi K, Isfeedvajani MS (2023) Multi-lead ECG heartbeat classification of heart disease based on HOG local feature descriptor. Comput Methods Programs Biomed Update. https:\/\/doi.org\/10.1016\/j.cmpbup.2023.100093","journal-title":"Comput Methods Programs Biomed Update"},{"issue":"3","key":"11577_CR92","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1111\/jce.15823","volume":"34","author":"M Shimojo","year":"2023","unstructured":"Shimojo M et al (2023) A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins. J Cardiovasc Electrophysiol 34(3):627\u2013637. https:\/\/doi.org\/10.1111\/jce.15823","journal-title":"J Cardiovasc Electrophysiol"},{"issue":"4","key":"11577_CR93","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1093\/eurheartj\/ehq437","volume":"32","author":"JT S\u00f8rensen","year":"2011","unstructured":"S\u00f8rensen JT et al (2011) Urban and rural implementation of pre-hospital diagnosis and direct referral for primary percutaneous coronary intervention in patients with acute ST-elevation myocardial infarction. Eur Heart J 32(4):430\u2013436","journal-title":"Eur Heart J"},{"issue":"6","key":"11577_CR94","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1093\/europace\/euac038","volume":"24","author":"E Svennberg","year":"2022","unstructured":"Svennberg E et al (2022) How to use digital devices to detect and manage arrhythmias: an EHRA practical guide. Europace 24(6):979\u20131005","journal-title":"Europace"},{"issue":"1","key":"11577_CR95","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1515\/bmt-2022-0406","volume":"69","author":"MB Terzi","year":"2024","unstructured":"Terzi MB, Arikan O (2024) Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram. Biomed Tech (Berl) 69(1):79\u2013109. https:\/\/doi.org\/10.1515\/bmt-2022-0406","journal-title":"Biomed Tech (Berl)"},{"issue":"12","key":"11577_CR96","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101875","volume":"5","author":"Y Tian","year":"2024","unstructured":"Tian Y et al (2024) Foundation model of ECG diagnosis: diagnostics and explanations of any form and rhythm on ECG. Cell Rep Med 5(12):101875. https:\/\/doi.org\/10.1016\/j.xcrm.2024.101875","journal-title":"Cell Rep Med"},{"key":"11577_CR97","doi-asserted-by":"publisher","DOI":"10.1136\/openhrt-2023-002414","author":"S Togo","year":"2023","unstructured":"Togo S et al (2023) Model for classification of heart failure severity in patients with hypertrophic cardiomyopathy using a deep neural network algorithm with a 12-lead electrocardiogram. Open Heart D. https:\/\/doi.org\/10.1136\/openhrt-2023-002414","journal-title":"Open Heart D"},{"issue":"1","key":"11577_CR98","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44\u201356","journal-title":"Nat Med"},{"issue":"12","key":"11577_CR99","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/jsen.2019.2896308","volume":"19","author":"RK Tripathy","year":"2019","unstructured":"Tripathy RK, Bhattacharyya A, Pachori RB (2019) A novel approach for detection of myocardial infarction from ECG signals of multiple electrodes. IEEE Sens J 19(12):4509\u20134517. https:\/\/doi.org\/10.1109\/jsen.2019.2896308","journal-title":"IEEE Sens J"},{"key":"11577_CR100","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/JTEHM.2022.3227204","volume":"11","author":"LM Tseng","year":"2023","unstructured":"Tseng LM, Chuang CY, Chua SK, Tseng VS (2023) Identification of coronary culprit lesion in ST elevation myocardial infarction by using deep learning. IEEE J Transl Eng Health Med 11:70\u201379. https:\/\/doi.org\/10.1109\/JTEHM.2022.3227204","journal-title":"IEEE J Transl Eng Health Med"},{"key":"11577_CR101","doi-asserted-by":"publisher","first-page":"2195922","DOI":"10.1155\/2021\/2195922","volume":"2021","author":"W Ullah","year":"2021","unstructured":"Ullah W, Siddique I, Zulqarnain RM, Alam MM, Ahmad I, Raza UA (2021) Classification of arrhythmia in heartbeat detection using deep learning. Comput Intell Neurosci 2021:2195922. https:\/\/doi.org\/10.1155\/2021\/2195922","journal-title":"Comput Intell Neurosci"},{"issue":"2","key":"11577_CR102","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCEP.120.009056","volume":"14","author":"RR van de Leur","year":"2021","unstructured":"van de Leur RR et al (2021) Discovering and visualizing disease-specific electrocardiogram features using deep learning: proof-of-concept in phospholamban gene mutation carriers. Circ Arrhythm Electrophysiol 14(2):e009056","journal-title":"Circ Arrhythm Electrophysiol"},{"issue":"3","key":"11577_CR103","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1631\/FITEE.1700413","volume":"20","author":"L-D Wang","year":"2019","unstructured":"Wang L-D, Zhou W, Xing Y, Liu N, Movahedipour M, Zhou X-G (2019) A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG. Front Inf Technol Electron Eng 20(3):405\u2013413","journal-title":"Front Inf Technol Electron Eng"},{"key":"11577_CR104","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106006","volume":"203","author":"J Wang","year":"2021","unstructured":"Wang J et al (2021) Automated ECG classification using a non-local convolutional block attention module. Comput Methods Programs Biomed 203:106006. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106006","journal-title":"Comput Methods Programs Biomed"},{"key":"11577_CR105","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.797207","volume":"9","author":"L Wu","year":"2022","unstructured":"Wu L et al (2022) Deep learning networks accurately detect ST-segment elevation myocardial infarction and culprit vessel. Front Cardiovasc Med 9:797207. https:\/\/doi.org\/10.3389\/fcvm.2022.797207","journal-title":"Front Cardiovasc Med"},{"key":"11577_CR106","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11185408","author":"L Wu","year":"2022","unstructured":"Wu L et al (2022) LASSO regression-based diagnosis of acute ST-Segment elevation myocardial infarction (STEMI) on Electrocardiogram (ECG). J Clin Med. https:\/\/doi.org\/10.3390\/jcm11185408","journal-title":"J Clin Med"},{"key":"11577_CR107","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104725","author":"P Xiong","year":"2023","unstructured":"Xiong P et al (2023) Detection of inferior myocardial infarction based on multi branch hybrid network. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2023.104725","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"11577_CR108","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1109\/TCBB.2022.3176905","volume":"20","author":"X Xu","year":"2023","unstructured":"Xu X, Xu H, Wang L, Zhang Y, Xaio F (2023) Hygeia: a multilabel deep learning-based classification method for imbalanced electrocardiogram data. IEEE\/ACM Trans Comput Biol Bioinform 20(4):2480\u20132493. https:\/\/doi.org\/10.1109\/TCBB.2022.3176905","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"3","key":"11577_CR109","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/tetci.2023.3235374","volume":"7","author":"S Yang","year":"2023","unstructured":"Yang S, Lian C, Zeng Z, Xu B, Zang J, Zhang Z (2023) A multi-view multi-scale neural network for multi-label ECG classification. IEEE Trans Emerg Top Comput Intell 7(3):648\u2013660. https:\/\/doi.org\/10.1109\/tetci.2023.3235374","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"11577_CR110","first-page":"1","volume-title":"Handbook of medical and health sciences in developing countries: education, practice, and research","author":"RK Zeidan","year":"2024","unstructured":"Zeidan RK, Farah R (2024) Myocardial infarctions in developing countries. Handbook of medical and health sciences in developing countries: education, practice, and research. Springer, Berlin, pp 1\u201330"},{"key":"11577_CR111","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104880","volume":"139","author":"P Zhang","year":"2021","unstructured":"Zhang P et al (2021) Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings. Comput Biol Med 139:104880. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104880","journal-title":"Comput Biol Med"},{"key":"11577_CR112","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104224","volume":"79","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Yi J, Chen A, Cheng L (2023) Cardiac arrhythmia classification by time\u2013frequency features inputted to the designed convolutional neural networks. Biomed Signal Process Control 79:104224","journal-title":"Biomed Signal Process Control"},{"key":"11577_CR113","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ijcard.2020.04.089","volume":"317","author":"Y Zhao","year":"2020","unstructured":"Zhao Y et al (2020) Early detection of ST-segment elevated myocardial infarction by artificial intelligence with 12-lead electrocardiogram. Int J Cardiol 317:223\u2013230. https:\/\/doi.org\/10.1016\/j.ijcard.2020.04.089","journal-title":"Int J Cardiol"},{"issue":"10","key":"11577_CR114","doi-asserted-by":"publisher","first-page":"1932","DOI":"10.1371\/journal.pone.0206170","volume":"13","author":"H Zhu","year":"2018","unstructured":"Zhu H, Pan Y, Cheng K-T, Huan R (2018) A lightweight piecewise linear synthesis method for standard 12-lead ECG signals based on adaptive region segmentation. PLoS ONE 13(10):1932\u20136203","journal-title":"PLoS ONE"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-026-11577-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-026-11577-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-026-11577-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:02:26Z","timestamp":1784527346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-026-11577-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":114,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["11577"],"URL":"https:\/\/doi.org\/10.1007\/s10462-026-11577-8","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"assertion":[{"value":"7 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"173"}}