ArticleBioengineering (Basel, Switzerland)2021
Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets.
Article in Bioengineering (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations.Scientific reports · 2026Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images.Bioengineering (Basel, Switzerland) · 2022Article
- Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context.Frontiers in physiology · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.
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