{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:17:58Z","timestamp":1773415078364,"version":"3.50.1"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00034-023-02550-9","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T12:02:02Z","timestamp":1702900922000},"page":"2273-2287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving the Efficiency of Automatic Cardiac Arrhythmias Classification by a Novel Patient-Specific Feature Space Mapping"],"prefix":"10.1007","volume":"43","author":[{"given":"Hamid","family":"Shafaatfar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5285-7685","authenticated-orcid":false,"given":"Mehdi","family":"Taghizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morteza","family":"Valizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Hossein","family":"Fatehi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,18]]},"reference":[{"key":"2550_CR1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ymeth.2021.04.021","volume":"202","author":"C Chen","year":"2022","unstructured":"C. Chen, Y. Lin, S. Lee, W. Tsai, T. Huang, Y. Liu, M. Cheng, C. Dai, Automated ECG classification based on 1D deep learning network Methods. Methods 202, 127\u2013135 (2022)","journal-title":"Methods"},{"key":"2550_CR2","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.compbiomed.2018.06.028","volume":"100","author":"SH Choi","year":"2018","unstructured":"S.H. Choi, H. Yoon, H.B. Kim, H.S. Kim, H.B. Kwon, S.M. Oh, Y.J. Lee, K.S. Park, Real-time apnea-hypopnea event detection during sleep by convolutional neural networks. Comput. Biol. Med. 100, 123\u2013131 (2018)","journal-title":"Comput. Biol. Med."},{"key":"2550_CR3","volume-title":"Deep Learning with Python","author":"F Chollet","year":"2018","unstructured":"F. Chollet, Deep Learning with Python (Manning Publications, Shelter Island, NY, 2018)"},{"key":"2550_CR4","volume-title":"Understanding Electrocardiography","author":"MB Conover","year":"2002","unstructured":"M.B. Conover, Understanding Electrocardiography (Elsevier, Amsterdam, 2002)"},{"key":"2550_CR5","doi-asserted-by":"crossref","unstructured":"A. Graves, A. Mohamed, and G. Hinton, Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (2013), pp. 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"2550_CR6","unstructured":"https:\/\/physionet.org\/content\/mitdb\/1.0.0\/"},{"key":"2550_CR7","unstructured":"S. Ioffe and C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning (2015), pp. 448\u2013456"},{"issue":"3","key":"2550_CR8","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2016","unstructured":"S. Kiranyaz, T. Ince, M. Gabbouj, Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664\u2013675 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2550_CR9","doi-asserted-by":"publisher","first-page":"106582","DOI":"10.1016\/j.cmpb.2021.106582","volume":"214","author":"Y Li","year":"2022","unstructured":"Y. Li, R. Qian, K. Li, Inter-patient arrhythmia classification with improved deep residual convolutional neural network. Comput. Methods Programs Biomed. 214, 106582 (2022)","journal-title":"Comput. Methods Programs Biomed."},{"key":"2550_CR10","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.compbiomed.2014.02.012","volume":"48","author":"RJ Martis","year":"2014","unstructured":"R.J. Martis, U.R. Acharya, H. Adeli, Current methods in electrocardiogram characterization. Comput. Biol. Med. 48, 133\u2013149 (2014)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"2550_CR11","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.bspc.2012.08.004","volume":"8","author":"RJ Martis","year":"2013","unstructured":"R.J. Martis, U.R. Acharya, K. Mandana, A. Ray, C. Chakraborty, Cardiac decision making using higher order spectra. Biomed. Signal Process. Control 8(2), 193\u2013203 (2013)","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"2550_CR12","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","volume":"8","author":"RJ Martis","year":"2013","unstructured":"R.J. Martis, U.R. Acharya, L.C. Min, Ecg beat classification using Pca, Lda, Ica and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437\u2013448 (2013)","journal-title":"Biomed. Signal Process. Control"},{"key":"2550_CR13","doi-asserted-by":"crossref","unstructured":"T. Mikolov, S. Kombrink, L. Burget, J. \u010cernock\u00fd, and S. Khudanpur, Extensions of recurrent neural network language model. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2011), pp. 5528\u20135531","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"2550_CR14","volume-title":"Cardiopulmonary Pharmacology for Respiratory Care","author":"J Moini","year":"2010","unstructured":"J. Moini, Cardiopulmonary Pharmacology for Respiratory Care (Jones & Bartlett Learning, Burlington, 2010)"},{"key":"2550_CR15","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.bspc.2019.01.018","volume":"50","author":"N Mourad","year":"2019","unstructured":"N. Mourad, ECG denoising algorithm based on group sparsity and singular spectrum analysis. Biomed. Signal Process. Control 50, 62\u201371 (2019)","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"2550_CR16","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.irbm.2019.12.001","volume":"41","author":"S Sahoo","year":"2020","unstructured":"S. Sahoo, M. Dash, S. Behera, S. Sabut, Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey. IRBM 41(4), 185\u2013194 (2020)","journal-title":"IRBM"},{"key":"2550_CR17","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.eswa.2018.12.037","volume":"122","author":"A Sellami","year":"2019","unstructured":"A. Sellami, H. Hwang, A robust deep convolutional neural network with batch-weighted loss for heartbeat classification. Expert Syst. Appl. 122, 75\u201384 (2019)","journal-title":"Expert Syst. Appl."},{"key":"2550_CR18","doi-asserted-by":"crossref","unstructured":"J. Takalo-Mattila, J. Kiljander, and J. Soininen, Inter-patient ECG classification using deep convolutional neural networks. 21st Euromicro Conference on Digital System Design (DSD) (2018), pp. 421\u2013425","DOI":"10.1109\/DSD.2018.00077"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-023-02550-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-023-02550-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-023-02550-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T12:16:44Z","timestamp":1709036204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-023-02550-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,18]]},"references-count":18,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2550"],"URL":"https:\/\/doi.org\/10.1007\/s00034-023-02550-9","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,18]]},"assertion":[{"value":"18 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2023","order":4,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}