Key result
A proposed cross-database learning framework using a two-dimensional beat-score-map representation achieved F1 scores of 0.9301 on the SPH dataset and 0.9267 on the PTB-XL dataset for ECG arrhythmia classification.
Why the study?
Cross-database ECG classification remains challenging due to variations in patient populations, recording conditions, and annotation granularity, with existing methods seldom utilizing fine-grained and coarse-grained labels simultaneously.
A novel cross-database learning framework using 2D beat-score-map representations demonstrates robust performance for ECG arrhythmia classification across heterogeneous datasets.
May support automated ECG classification development; leaves open clinical adoption pending prospective validation.
Cross-database electrocardiogram (ECG) classification remains a critical challenge due to variations in patient populations, recording conditions, and annotation granularity. Existing methodologies for ECG arrhythmia classification have primarily utilized datasets with either fine-grained or coarse-grained labels, but seldom both simultaneously. Fine-grained labels provide beat-level annotations, whereas coarse-grained labels offer only record-level labels. In this study, we propose an innovative cross-database learning framework that utilizes both fine-grained and coarse-grained labels in tandem, thereby enhancing classification performance across heterogeneous datasets. Specifically, our approach begins with the pretraining of a CNN-based beat classifier that takes ECG signals as the input and predicts beat types on a finely labeled dataset, namely the MIT-BIH Arrhythmia Database (MITDB). The pretrained model is then fine-tuned using weakly supervised learning on two coarsely labeled datasets: the SPH one, which contains four rhythm classes, and the PTB-XL one, which involves binary classification between the sinus rhythm (SR) and atrial fibrillation (AFIB). Once the beat classifier is adapted to a new dataset, it generates a two-dimensional beat-score-map (BSM) representation from the input ECG signal. This 2D BSM is subsequently utilized as the input for arrhythmia rhythm classification. The proposed method achieves F1 scores of 0.9301 on the SPH dataset and 0.9267 on the PTB-XL dataset, corresponding to the multi-class and binary rhythm classification tasks described above. These results demonstrate a robust cross-database classification of complex cardiac arrhythmia rhythms. Furthermore, t-SNE visualizations of the 2D BSM representations, after adaptation to the coarsely labeled SPH and PTB-XL datasets, validate how our method significantly enhances the ability to differentiate between various arrhythmia rhythm types, thus highlighting its effectiveness in cross-database ECG analysis.
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Lee et al. (2025) studied Arrhythmia. Cross-database learning framework using two-dimensional beat-score-map (BSM) representation was evaluated on F1 score for arrhythmia rhythm classification. A proposed cross-database learning framework using a two-dimensional beat-score-map representation achieved F1 scores of 0.9301 on the SPH dataset and 0.9267 on the PTB-XL dataset for ECG arrhythmia classification.
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