## 4 Results
### Arrhythmia Classification Using DA-BSMs for Coarse-Grained Databases
#### 4.2.1 Evaluation with SPH Database  
Table 4 summarizes the classification performance for four arrhythmia rhythm types on the SPH database. The proposed DA-BSM approach achieved a precision, recall, F1-score, and accuracy of 0.9306, 0.9299, 0.9301, and 0.9380, respectively, clearly outperforming the baseline BSM method (denoted as \"Pretraining-only\" in Table 4). These results demonstrate the effectiveness of our weakly supervised domain adaptation in improving classification performance, particularly in scenarios with limited annotations. When trained exclusively on the coarsely annotated SPH dataset (denoted as \"Coarse-only\" in Table 4), the accuracy was 0.8628. Pretraining solely on the finely annotated MITDB dataset increased the accuracy to 0.9243, while incorporating weakly supervised adaptation after pretraining further improved the accuracy to 0.9380. This performance progression underscores the advantage of the DA-BSM framework in effectively utilizing both fine-grained and coarse-grained annotations, thus ensuring strong adaptability and generalization across diverse ECG database environments. Figure 5: Training and validation curves for the rhythm classifier using DA-BSM inputs. Left (**A**): SPH (4-class). Right (**B**): PTB-XL (binary). Further analysis of the SPH database revealed that the DA-BSM method achieved performance improvements, especially for complex arrhythmias such as GSVT and AFIB, which involve prominent morphological variations and interval irregularities. Table 5 summarizes the detailed F1-scores for these complex rhythms, clearly demonstrating the enhanced adaptability of the DA-BSM method compared to the baseline methods. These improvements confirm that the weakly supervised domain adaptation strategy effectively captures subtle morphological differences and dynamic signal characteristics across patient populations and varying measurement conditions, thereby addressing the inherent variability of ECG signals. To validate the effectiveness of our DA-BSM approach, Table 6 presents a performance comparison with state-of-the-art ECG arrhythmia classification methods. While the existing methods typically require multiple leads or extensive preprocessing steps, the DA-BSM method demonstrates significant performance improvement using only a single lead, highlighting its practicality and computational efficiency. For instance, [PERSON] et al. [37] reported a high accuracy of 0.952, but their method requires all 12 leads and generates large-scale, high-resolution image representations (e.g., two 299\(\times\)299 images per sample), resulting in a more complex and resource-intensive processing pipeline. [PERSON] et al. [38] achieved an accuracy of 0.907, but their approach depends on additional demographic information (e.g., age and gender), limiting practicality under resource-constrained conditions. [PERSON] et al. [39] simplified the input requirement to a single lead; however, their reported F1-score of 0.86 indicates limitations in accurately classifying complex arrhythmias and achieving robust generalizability. In contrast, our proposed DA-BSM method effectively achieves a superior classification performance with an F1-score of 0.9301, requiring minimal preprocessing and utilizing a single ECG lead, thereby emphasizing its potential advantage in real-world clinical applications. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Pretrained** & **WSL** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Coarse-only & & & 0.8483 & 0.8462 & 0.8463 & 0.8628 \\ \hline Pretraining-only & & & 0.9148 & 0.9136 & 0.9140 & 0.9243 \\ \hline DA-BSM & & & 0.9306 & 0.9299 & 0.9301 & 0.9380 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification performance of the proposed DA-BSM method on the SPH database (4-class classification: SR, AFIB, GSVT, SB; number of samples: SR = 2225, AFIB = 2225, GSVT = 2307, SB = 3889).