{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:46:11Z","timestamp":1742935571756,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031625190"},{"type":"electronic","value":"9783031625206"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-62520-6_20","type":"book-chapter","created":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:02:00Z","timestamp":1725062520000},"page":"171-178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Meta Learning in Quality Assessment of Wearable Electrocardiogram Recordings"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0956-1440","authenticated-orcid":false,"given":"Alvaro","family":"Huerta","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2343-3186","authenticated-orcid":false,"given":"Arturo","family":"Mart\u00ednez-Rodrigo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0573-9122","authenticated-orcid":false,"given":"Miguel","family":"Guimar\u00e2es","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6650-0388","authenticated-orcid":false,"given":"Davide","family":"Carneiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3364-6380","authenticated-orcid":false,"given":"Jos\u00e9 J.","family":"Rieta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0942-3638","authenticated-orcid":false,"given":"Ra\u00fal","family":"Alcaraz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"20_CR1","unstructured":"OMS. Cardiovascular Diseases (2023). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds). Accessed on 23 Sep 2023"},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Orini, M., et al.: Premature atrial and ventricular contractions detected on wearable-format electrocardiograms and prediction of cardiovascular events. Eur. Heart J.-Digital Health 4(2), 112\u2013118 (2023). https:\/\/doi.org\/10.1093\/ehjdh\/ztad007","DOI":"10.1093\/ehjdh\/ztad007"},{"issue":"4","key":"20_CR3","first-page":"211","volume":"57","author":"P Stachon","year":"2015","unstructured":"Stachon, P., Ahrens, I., Faber, T., Bode, C., Zirlik, A.: Asymptomatic atrial fibrillation and risk of stroke. Panminerva Med. 57(4), 211\u2013215 (2015)","journal-title":"Panminerva Med."},{"issue":"4","key":"20_CR4","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1049\/htl.2016.0100","volume":"4","author":"S Nagai","year":"2017","unstructured":"Nagai, S., Anzai, D., Wang, J.: Motion artifact removals for wearable ECG using stationary wavelet transform. Healthcare Technol. Lett. 4(4), 138\u2013141 (2017). https:\/\/doi.org\/10.1049\/htl.2016.0100","journal-title":"Healthcare Technol. Lett."},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Zhao, Z., et al.: Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks. IEEE Access 7, 34060\u201334067 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2900719","DOI":"10.1109\/ACCESS.2019.2900719"},{"key":"20_CR6","doi-asserted-by":"publisher","unstructured":"Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A.E.W., Clifford, G.D.: Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters. IEEE Trans. Biomed. Eng. 62(9), 2125\u20132134 (2015). https:\/\/doi.org\/10.1109\/TBME.2015.2402236","DOI":"10.1109\/TBME.2015.2402236"},{"issue":"6","key":"20_CR7","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.1109\/TBME.2013.2240452","volume":"60","author":"J Behar","year":"2013","unstructured":"Behar, J., Oster, J., Li, Q., Clifford, G.D.: ECG signal quality during arrhythmia and its application to false alarm reduction. IEEE Trans. Biomed. Eng. 60(6), 1660\u20131666 (2013). https:\/\/doi.org\/10.1109\/TBME.2013.2240452","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"20_CR8","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1109\/JBHI.2017.2686436","volume":"22","author":"U Satija","year":"2018","unstructured":"Satija, U., Ramkumar, B., Manikandan, M.S.: Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J. Biomed. Health Inform. 22(3), 722\u2013732 (2018). https:\/\/doi.org\/10.1109\/JBHI.2017.2686436","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","volume":"161","author":"O Faust","year":"2018","unstructured":"Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: a review. Comput. Methods Programs Biomed. 161, 1\u201313 (2018). https:\/\/doi.org\/10.1016\/j.cmpb.2018.04.005","journal-title":"Comput. Methods Programs Biomed."},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Fu, L., Gu, L.: A cascaded convolutional neural network for assessing signal quality of dynamic ECG. Comput. Math. Methods Med. 2019 (2019). https:\/\/doi.org\/10.1155\/2019\/7095137","DOI":"10.1155\/2019\/7095137"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Yoon, D., Lim, H. S., Jung, K., Kim, T.Y., Lee S.: Deep learning-based electrocardiogram signal noise detection and screening model. Healthcare Inf. Res. 25(3), 201\u2013211 (2019). https:\/\/doi.org\/10.4258\/hir.2019.25.3.201","DOI":"10.4258\/hir.2019.25.3.201"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Huerta, \u00c1., Mart\u0131\u0301nez-Rodrigo, A., Bertomeu-Gonz\u00e1lez, V., Quesada, A., Rieta, J.J., Alcaraz, R.: a deep learning approach for featureless robust quality assessment of intermittent atrial fibrillation recordings from portable and wearable devices. Entropy (Basel) 22(7) (2020). https:\/\/doi.org\/10.3390\/e22070733","DOI":"10.3390\/e22070733"},{"issue":"9","key":"20_CR13","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1088\/0967-3334\/33\/9\/1419","volume":"33","author":"GD Clifford","year":"2012","unstructured":"Clifford, G.D., Behar, J., Li, Q., Rezek, I.: Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol. Meas. 33(9), 1419 (2012). https:\/\/doi.org\/10.1088\/0967-3334\/33\/9\/1419","journal-title":"Physiol. Meas."},{"key":"20_CR14","doi-asserted-by":"publisher","unstructured":"Clifford, G.D., et al.: AF Classification from a short single lead ECG recording: the PhysioNet\/Computing in cardiology challenge 2017. Comput. Cardiol. 44 (2017). https:\/\/doi.org\/10.22489\/CinC.2017.065-469","DOI":"10.22489\/CinC.2017.065-469"},{"key":"20_CR15","doi-asserted-by":"publisher","first-page":"104164","DOI":"10.1016\/j.compbiomed.2020.104164","volume":"130","author":"A Albaba","year":"2021","unstructured":"Albaba, A., Sim\u00f5es-Capela, N., Wang, Y., Hendriks, R.C., De Raedt, W., Van Hoof, C.: Assessing the signal quality of electrocardiograms from varied acquisition sources: a generic machine learning pipeline for model generation. Comput. Biol. Med. 130, 104164 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104164","journal-title":"Comput. Biol. Med."},{"key":"20_CR16","unstructured":"Alcoba\u00e7a, E., Siqueira, F., Rivolli, A., Garcia, L.P.F., Oliva, J.T., de Carvalho, A.C.P.L.F.: MFE: towards reproducible meta-feature extraction. J. Mach. Learn. Res. 21(111), 1\u20135 (2020)"},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"108101","DOI":"10.1016\/j.knosys.2021.108101","volume":"240","author":"A Rivolli","year":"2022","unstructured":"Rivolli, A., Garcia, L.P.F., Soares, C., Vanschoren, J., de Carvalho, A.C.P.L.F.: Meta-features for meta-learning. Knowl.-Based Syst. 240, 108101 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2021.108101","journal-title":"Knowl.-Based Syst."},{"issue":"03","key":"20_CR18","doi-asserted-by":"publisher","first-page":"2350011","DOI":"10.1142\/S0129065723500119","volume":"33","author":"G Palumbo","year":"2023","unstructured":"Palumbo, G., Carneiro, D., Guimares, M., Alves, V., Novais, P.: Algorithm recommendation and performance prediction using meta-learning. Int. J. Neural Syst. 33(03), 2350011 (2023). https:\/\/doi.org\/10.1142\/S0129065723500119","journal-title":"Int. J. Neural Syst."},{"key":"20_CR19","doi-asserted-by":"publisher","unstructured":"Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.M., T.R.I.P.O.D. Group.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Circulation 131(2), 211\u2013219 (2015). https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.114.014508","DOI":"10.1161\/CIRCULATIONAHA.114.014508"}],"container-title":["IFMBE Proceedings","Advances in Digital Health and Medical Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62520-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T14:07:45Z","timestamp":1730988465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62520-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031625190","9783031625206"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62520-6_20","relation":{},"ISSN":["1680-0737","1433-9277"],"issn-type":[{"type":"print","value":"1680-0737"},{"type":"electronic","value":"1433-9277"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EHB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on e-Health and Bioengineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bucharest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Romania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ehb2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ehbconference.ro\/Home.aspx","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}