{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:13:20Z","timestamp":1790244800382,"version":"4.1.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032382597","type":"print"},{"value":"9783032382603","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-38260-3_36","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:56:31Z","timestamp":1790243791000},"page":"376-385","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Demographic-Conditioned State Space Models for\u00a0ECG-Based Age Estimation"],"prefix":"10.1007","author":[{"given":"Benjamin","family":"Bracke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Stang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B\u00f6rge","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph M.","family":"Friedrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","unstructured":"Ansari, M.Y., Qaraqe, M., Charafeddine, F., Serpedin, E., Righetti, R., Qaraqe, K.: Estimating age and gender from electrocardiogram signals: a comprehensive review of the past decade. Artif. Intell. Med. 146, 102690 (2023). https:\/\/doi.org\/10.1016\/j.artmed.2023.102690","DOI":"10.1016\/j.artmed.2023.102690"},{"key":"36_CR2","doi-asserted-by":"publisher","unstructured":"Attia, Z.I., et al.: Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs. Circ. Arrhythm. Electrophysiol. 12(9), e007284 (2019). https:\/\/doi.org\/10.1161\/CIRCEP.119.007284","DOI":"10.1161\/CIRCEP.119.007284"},{"issue":"6","key":"36_CR3","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.jelectrocard.2017.06.006","volume":"50","author":"L Bacharova","year":"2017","unstructured":"Bacharova, L., Estes, E.H.: Left ventricular hypertrophy by the surface ECG. J. Electrocardiol. 50(6), 906\u2013908 (2017). https:\/\/doi.org\/10.1016\/j.jelectrocard.2017.06.006","journal-title":"J. Electrocardiol."},{"key":"36_CR4","doi-asserted-by":"publisher","unstructured":"Ball, R.L., Feiveson, A.H., Schlegel, T.T., Starc, V., Dabney, A.R.: Predicting \u201cheart age\u201d using electrocardiography. J. Pers. Med. 4(1), 65\u201378 (2014). https:\/\/doi.org\/10.3390\/jpm4010065","DOI":"10.3390\/jpm4010065"},{"key":"36_CR5","unstructured":"Biernacka, A., Frangogiannis, N.G.: Aging and cardiac fibrosis. Aging Dis. 2(2), 158\u2013173 (2011). https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC3153299\/"},{"key":"36_CR6","doi-asserted-by":"publisher","unstructured":"Bulpitt, C.J., Rajkumar, C., Cameron, J.D.: Vascular compliance as a measure of biological age. J. Am. Geriatr. Soc. 47(6), 657\u2013663 (1999). https:\/\/doi.org\/10.1111\/j.1532-5415.1999.tb01586.x","DOI":"10.1111\/j.1532-5415.1999.tb01586.x"},{"key":"36_CR7","doi-asserted-by":"publisher","unstructured":"Cooney, M.T., et al.: SCORE and FINRISK investigators: cardiovascular risk age: concepts and practicalities. Heart 98(12), 941\u2013946 (2012). https:\/\/doi.org\/10.1136\/heartjnl-2011-301478","DOI":"10.1136\/heartjnl-2011-301478"},{"key":"36_CR8","unstructured":"Dao, T., Gu, A.: Transformers are SSMs: generalized models and efficient algorithms through structured state space duality. In: International Conference on Machine Learning. No. abs\/2405.21060 (2024)"},{"key":"36_CR9","doi-asserted-by":"publisher","unstructured":"Dhingra, R., Vasan, R.S.: Age as a risk factor. Med. Clin. North Am. 96(1), 87\u201391 (2012). https:\/\/doi.org\/10.1016\/j.mcna.2011.11.003","DOI":"10.1016\/j.mcna.2011.11.003"},{"key":"36_CR10","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. http:\/\/arxiv.org\/abs\/2312.00752, arXiv:2312.00752 [cs] (2024)"},{"key":"36_CR11","unstructured":"Gu, A., Goel, K., Re, C.: Efficiently modeling long sequences with structured state spaces. No. abs\/2111.00396 (2021)"},{"key":"36_CR12","doi-asserted-by":"publisher","unstructured":"Hamczyk, M.R., Nevado, R.M., Barettino, A., Fuster, V., Andr\u00e9s, V.: Biological versus chronological aging: JACC focus seminar. J. Am. Coll. Cardiol. 75(8), 919\u2013930 (2020). https:\/\/doi.org\/10.1016\/j.jacc.2019.11.062","DOI":"10.1016\/j.jacc.2019.11.062"},{"key":"36_CR13","doi-asserted-by":"publisher","unstructured":"Hempel, P., et al.: Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study. NPJ Digit. Med. 8(1), 25 (2025). https:\/\/doi.org\/10.1038\/s41746-024-01428-7","DOI":"10.1038\/s41746-024-01428-7"},{"key":"36_CR14","doi-asserted-by":"publisher","unstructured":"Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35(1), 73\u2013101 (1964). https:\/\/doi.org\/10.1214\/aoms\/1177703732","DOI":"10.1214\/aoms\/1177703732"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., Teh, Y.W.: Set transformer: a framework for attention-based permutation-invariant neural networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 3744\u20133753 (2019). https:\/\/proceedings.mlr.press\/v97\/lee19d.html","DOI":"10.5699\/modelangrevi.114.3.0571"},{"key":"36_CR16","doi-asserted-by":"publisher","unstructured":"Lima, E.M., et al.: Deep neural network-estimated electrocardiographic age as a mortality predictor. Nat. Commun. 12(1), 5117 (2021). https:\/\/doi.org\/10.1038\/s41467-021-25351-7","DOI":"10.1038\/s41467-021-25351-7"},{"key":"36_CR17","doi-asserted-by":"publisher","unstructured":"Lin, J., et al.: MSECG: incorporating mamba for robust and efficient ECG super-resolution. In: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135. IEEE, Hyderabad (2025). https:\/\/doi.org\/10.1109\/ICASSP49660.2025.10890523","DOI":"10.1109\/ICASSP49660.2025.10890523"},{"key":"36_CR18","doi-asserted-by":"publisher","unstructured":"Lindow, T., et al.: Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival. Eur. Heart J. - Digit. Health 4(5), 384\u2013392 (2023). https:\/\/doi.org\/10.1093\/ehjdh\/ztad045","DOI":"10.1093\/ehjdh\/ztad045"},{"key":"36_CR19","doi-asserted-by":"publisher","unstructured":"Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4172\u20134182. IEEE, Paris (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.00387","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"36_CR20","doi-asserted-by":"publisher","unstructured":"Qiang, Y., Dong, X., Liu, X., Yang, Y., Hu, F., Wang, R.: ECGMamba: towards ECG classification with state space models. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 6498\u20136505 (2024). https:\/\/doi.org\/10.1109\/BIBM62325.2024.10822754","DOI":"10.1109\/BIBM62325.2024.10822754"},{"key":"36_CR21","doi-asserted-by":"publisher","unstructured":"Ribeiro, A.H., et al.: CODE dataset (2021). https:\/\/doi.org\/10.17044\/SCILIFELAB.15169716","DOI":"10.17044\/SCILIFELAB.15169716"},{"key":"36_CR22","doi-asserted-by":"publisher","unstructured":"Ribeiro, A.L.P., et al.: Tele-electrocardiography and bigdata: the CODE (clinical outcomes in digital electrocardiography) study. J. Electrocardiol. 57, S75\u2013S78 (2019). https:\/\/doi.org\/10.1016\/j.jelectrocard.2019.09.008","DOI":"10.1016\/j.jelectrocard.2019.09.008"},{"key":"36_CR23","doi-asserted-by":"publisher","unstructured":"Ribeiro, A.H., et al.: CODE-15%: a large scale annotated dataset of 12-lead ECGs (2021). https:\/\/doi.org\/10.5281\/ZENODO.4916206","DOI":"10.5281\/ZENODO.4916206"},{"key":"36_CR24","doi-asserted-by":"publisher","unstructured":"Ribeiro, A.H., et al.: Sami-trop: 12-lead ECG traces with age and mortality annotations (2021). https:\/\/doi.org\/10.5281\/ZENODO.4905618","DOI":"10.5281\/ZENODO.4905618"},{"key":"36_CR25","doi-asserted-by":"publisher","unstructured":"Tiezzi, M., Casoni, M., Betti, A., Gori, M., Melacci, S.: State-space modeling in long sequence processing: a survey on recurrence in the transformer era. Neural Netw. 193, 108039 (2026). https:\/\/doi.org\/10.1016\/j.neunet.2025.108039","DOI":"10.1016\/j.neunet.2025.108039"},{"key":"36_CR26","unstructured":"World Health Organization: World health statistics 2019: monitoring health for the SDGs, sustainable development goals (2019). https:\/\/apps.who.int\/iris\/handle\/10665\/324835"},{"key":"36_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, H., et al.: S2M2ECG: spatio-temporal bi-directional state space model enabled multi-branch mamba for ECG (2025). https:\/\/doi.org\/10.48550\/arXiv.2509.03066","DOI":"10.48550\/arXiv.2509.03066"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38260-3_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:56:35Z","timestamp":1790243795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38260-3_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032382597","9783032382603"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38260-3_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"assertion":[{"value":"25 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This secondary analysis of anonymized data complies with all relevant ethical regulations. The CODE study was approved by the Universidade Federal de Minas Gerais Ethics Committee (49368496317.7.0000.5149) and SaMi-Trop study by the Brazilian National Institutional Review Board (CONEP 179.685\/2012).","order":1,"name":"Ethics","label":"Ethics Declarations","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}