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Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification
- Source :
- Caring is Sharing – Exploiting the Value in Data for Health and Innovation ISBN: 9781643683881
- Publication Year :
- 2023
- Publisher :
- IOS Press, 2023.
-
Abstract
- 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.
Details
- ISBN :
- 978-1-64368-388-1
- ISBNs :
- 9781643683881
- Database :
- OpenAIRE
- Journal :
- Caring is Sharing – Exploiting the Value in Data for Health and Innovation ISBN: 9781643683881
- Accession number :
- edsair.doi...........6f8ba28d770d60e22cf2d2a2ff6b9854