{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:48:48Z","timestamp":1747216128054,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643683881"},{"type":"electronic","value":"9781643683898"}],"license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,18]]},"abstract":"<jats:p>Deep Learning architectures for time series require a large number of training samples, however traditional sample size estimation for sufficient model performance is not applicable for machine learning, especially in the field of electrocardiograms (ECGs). This paper outlines a sample size estimation strategy for binary classification problems on ECGs using different deep learning architectures and the large publicly available PTB-XL dataset, which includes 21801 ECG samples. This work evaluates binary classification tasks for Myocardial Infarction (MI), Conduction Disturbance (CD), ST\/T Change (STTC), and Sex. All estimations are benchmarked across different architectures, including XResNet, Inception-, XceptionTime and a fully convolutional network (FCN). The results indicate trends for required sample sizes for given tasks and architectures, which can be used as orientation for future ECG studies or feasibility aspects.<\/jats:p>","DOI":"10.3233\/shti230099","type":"book-chapter","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T04:43:36Z","timestamp":1684471416000},"source":"Crossref","is-referenced-by-count":1,"title":["Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9043-7844","authenticated-orcid":false,"given":"Lucas","family":"Bickmann","sequence":"first","affiliation":[{"name":"Institute of Medical Informatics, University of M\u00fcnster, M\u00fcnster, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7626-8853","authenticated-orcid":false,"given":"Lucas","family":"Plagwitz","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of M\u00fcnster, M\u00fcnster, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7206-3719","authenticated-orcid":false,"given":"Julian","family":"Varghese","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of M\u00fcnster, M\u00fcnster, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Caring is Sharing \u2013 Exploiting the Value in Data for Health and Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T10:56:32Z","timestamp":1685530592000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"ISBN":["9781643683881","9781643683898"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230099","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}