{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:54:54Z","timestamp":1790286894914,"version":"4.1.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP09258659"],"award-info":[{"award-number":["AP09258659"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cardiovasc Eng Tech"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s13239-024-00719-0","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T22:03:46Z","timestamp":1709244226000},"page":"346-358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["12-Lead ECG Reconstruction Based on Data From the First Limb Lead"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5057-2942","authenticated-orcid":false,"given":"Alexey","family":"Savostin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9586-2310","authenticated-orcid":false,"given":"Kayrat","family":"Koshekov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1201-1247","authenticated-orcid":false,"given":"Yekaterina","family":"Ritter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7042-4480","authenticated-orcid":false,"given":"Galina","family":"Savostina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-5834","authenticated-orcid":false,"given":"Dmitriy","family":"Ritter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"719_CR1","doi-asserted-by":"publisher","unstructured":"Ribeiro Pinto, J., J. S. Cardoso and A. Louren\u00e7o Evolution, Current Challenges, and Future Possibilities in ECG Biometrics. IEEE Access. 2018; https:\/\/doi.org\/10.1109\/ACCESS.2018.2849870","DOI":"10.1109\/ACCESS.2018.2849870"},{"key":"719_CR2","doi-asserted-by":"publisher","unstructured":"Gregory A. Roth et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u20132019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020; https:\/\/doi.org\/10.1016\/j.jacc.2020.11.010","DOI":"10.1016\/j.jacc.2020.11.010"},{"key":"719_CR3","doi-asserted-by":"publisher","DOI":"10.6515\/ACS.202101_37(1).20200712A","author":"GH Smith","year":"2021","unstructured":"Smith, G. H., D. J. Van den Heever, and W. Swart. The Reconstruction of a 12-Lead Electrocardiogram from a Reduced Lead Set Using a Focus Time-Delay Neural Network. Acta Cardiol Sin. 2021. https:\/\/doi.org\/10.6515\/ACS.202101_37(1).20200712A.","journal-title":"Acta Cardiol Sin."},{"key":"719_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/s20113278","author":"J Sohn","year":"2020","unstructured":"Sohn, J., S. Yang, J. Lee, Y. Ku, and H. C. Kim. Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network. Sensors. 2020. https:\/\/doi.org\/10.3390\/s20113278.","journal-title":"Sensors."},{"key":"719_CR5","doi-asserted-by":"publisher","unstructured":"Lee, J., K. Oh, B. Kim and S. K. Yoo Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network,\" in IEEE Journal of Biomedical and Health Informatics. 2020; https:\/\/doi.org\/10.1109\/JBHI.2019.2936583","DOI":"10.1109\/JBHI.2019.2936583"},{"key":"719_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/s21165542","author":"A Grande-Fidalgo","year":"2021","unstructured":"Grande-Fidalgo, A., J. Calpe, M. Red\u00f3n, C. Mill\u00e1n-Navarro, and E. Soria-Olivas. Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition. Sensors. 2021. https:\/\/doi.org\/10.3390\/s21165542.","journal-title":"Sensors."},{"key":"719_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-02063-6","author":"SC Beco","year":"2022","unstructured":"Beco, S. C., J. R. Pinto, and J. S. Cardoso. Electrocardiogram lead conversion from single-lead blindly-segmented signals. BMC Med Inform Decis Mak. 2022. https:\/\/doi.org\/10.1186\/s12911-022-02063-6.","journal-title":"BMC Med Inform Decis Mak."},{"key":"719_CR8","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention. MICCAI 2015. 2015; https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"719_CR9","doi-asserted-by":"publisher","unstructured":"Matyschik, M., H. Mauranen, P. Bonizzi and J. Karel. Feasibility of ECG Reconstruction From Minimal Lead Sets Using Convolutional Neural Networks. Computing in Cardiology 2020, 2020; https:\/\/doi.org\/10.22489\/CinC.2020.164","DOI":"10.22489\/CinC.2020.164"},{"key":"719_CR10","unstructured":"Maxim Integrated. MAX86150 Integrated Photoplethysmogram and Electrocardiogram Bio-Sensor Module For Mobile Health Datasheet. 19-8402; Rev 2; 12\/18. 30.11.2022. https:\/\/datasheets.maximintegrated.com\/en\/ds\/MAX86150.pdf. Accessed 15 Aug 2023."},{"key":"719_CR11","doi-asserted-by":"crossref","unstructured":"Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos Fundamentals of Machine Learning and Deep Learning in Medicine. Springer. 2022.","DOI":"10.1007\/978-3-031-19502-0"},{"key":"719_CR12","doi-asserted-by":"publisher","unstructured":"Wagner, P., Strodthoff, N., Bousseljot, RD. et al. PTB-XL, a large publicly available electrocardiography dataset. Sci Data 7. 2020; https:\/\/doi.org\/10.1038\/s41597-020-0495-6","DOI":"10.1038\/s41597-020-0495-6"},{"key":"719_CR13","doi-asserted-by":"publisher","unstructured":"Ahilan Appathurai, J. Jerusalin Carol, C. Raja, S.N. Kumar, Ashy V. Daniel, A. Jasmine Gnana Malar, A. Lenin Fred, Sujatha Krishnamoorthy A study on ECG signal characterization and practical implementation of some ECG characterization techniques, Measurement. 2019; https:\/\/doi.org\/10.1016\/j.measurement.2019.02.040","DOI":"10.1016\/j.measurement.2019.02.040"},{"key":"719_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2007.01.024","author":"P Kligfield","year":"2007","unstructured":"Kligfield, P., et al. Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol. 2007. https:\/\/doi.org\/10.1016\/j.jacc.2007.01.024.","journal-title":"J Am Coll Cardiol."},{"key":"719_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s11018-018-1494-2","author":"AA Savostin","year":"2018","unstructured":"Savostin, A. A., D. V. Ritter, G. V. Savostina, et al. Comparative Analysis of Algorithms for Elimination of Low-Frequency Noise in Electrocardio-Signals. Meas Tech. 2018. https:\/\/doi.org\/10.1007\/s11018-018-1494-2.","journal-title":"Meas Tech."},{"key":"719_CR16","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s40031-019-00398-9","volume":"100","author":"V Gupta","year":"2019","unstructured":"Gupta, V., and M. Mittal. QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases. J. Inst. Eng. India Ser. B. 100:489\u2013497, 2019. https:\/\/doi.org\/10.1007\/s40031-019-00398-9.","journal-title":"J. Inst. Eng. India Ser. B"},{"key":"719_CR17","volume-title":"Pattern Recognition and Machine Learning","author":"Christopher M Bishop","year":"2006","unstructured":"Bishop, Christopher M. Pattern Recognition and Machine Learning. New York: Springer, 2006."},{"key":"719_CR18","doi-asserted-by":"publisher","unstructured":"Andoni A. and P. Indyk Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions. 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS\u201906). 2006l https:\/\/doi.org\/10.1109\/FOCS.2006.49","DOI":"10.1109\/FOCS.2006.49"},{"key":"719_CR19","doi-asserted-by":"publisher","unstructured":"Pedregosa et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; https:\/\/doi.org\/10.48550\/arXiv.1201.0490","DOI":"10.48550\/arXiv.1201.0490"},{"key":"719_CR20","doi-asserted-by":"publisher","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. & others (2016). TensorFlow: A System for Large-Scale Machine Learning. OSDI. 2016; https:\/\/doi.org\/10.48550\/arXiv.1605.08695","DOI":"10.48550\/arXiv.1605.08695"},{"key":"719_CR21","doi-asserted-by":"publisher","unstructured":"Kingma D., Ba J . Adam: A Method for Stochastic Optimization. he 3rd International Conference for Learning Representations, 2015. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"719_CR22","unstructured":"Harris, David and Harris, Sarah (2012-08-07). Digital design and computer architecture (2nd ed.). San Francisco, Calif.: Morgan Kaufmann."},{"key":"719_CR23","unstructured":"Hampton, John.\u00a0The ECG Made Easy. 9th Edition.\u00a0Elsevier, 2019."},{"issue":"4","key":"719_CR24","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.5937\/jaes0-38656","volume":"20","author":"S Askar","year":"2022","unstructured":"Askar, S., T. Amandyk, J. Nutpulla, K. Kairat, et al. Design of a complex of medical service robots and analysis of transmission characteristics of drives. Journal of Applied Engineering Science. 20(4):1242\u20131253, 2022. https:\/\/doi.org\/10.5937\/jaes0-38656.","journal-title":"Journal of Applied Engineering Science"},{"issue":"2(119)","key":"719_CR25","doi-asserted-by":"publisher","first-page":"62","DOI":"10.15587\/1729-4061.2022.265066","volume":"5","author":"A Savostin","year":"2022","unstructured":"Savostin, A., A. Tuleshov, K. Koshekov, G. Savostina, and A. Largin. Devising a method for predicting a blood pressure level based on electrocardiogram and photoplethysmogram signals. Eastern-European Journal of Enterprise Technologies. 5(2(119)):62\u201374, 2022. https:\/\/doi.org\/10.15587\/1729-4061.2022.265066.","journal-title":"Eastern-European Journal of Enterprise Technologies"}],"container-title":["Cardiovascular Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13239-024-00719-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13239-024-00719-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13239-024-00719-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T16:26:30Z","timestamp":1720715190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13239-024-00719-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,29]]},"references-count":25,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["719"],"URL":"https:\/\/doi.org\/10.1007\/s13239-024-00719-0","relation":{},"ISSN":["1869-408X","1869-4098"],"issn-type":[{"value":"1869-408X","type":"print"},{"value":"1869-4098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,29]]},"assertion":[{"value":"17 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest regarding this research, including financial, personal, authorship, or any other conflicts that could affect the research and its results presented in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}