## 4 Results
### Arrhythmia Classification Using DA-BSMs for Coarse-Grained Databases
#### 4.2.2 Evaluation with PTB-XL Database  
Table 7 provides a classification performance comparison for the PTB-XL database, revealing consistent trends with the previous results; the DA-BSM approach significantly outperformed the baseline methods. The DA-BSM effectively mitigated domain differences, demonstrating strong robustness and adaptability across datasets. An ablation study further assessed the individual contributions of pretraining (fine-grained annotations from the MITDB) and WSL-based adaptation. The results indicate that combining pretraining with WSL-based adaptation yielded the best classification performance, emphasizing the complementary role of these components in handling coarse-grained annotations. In particular, our results on the PTB-XL database showed superior performance in classifying AFIB, where the quasi-periodic rhythm characteristics were effectively captured and adapted. Table 8 specifically highlights the improvements achieved by the DA-BSM method in classifying the SR and AFIB rhythm types. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Features** & **Classifier** & **Leads** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline [PERSON] et al. [37] &
\begin{tabular}{c} Scalogram + \\ grayscale image \\ \end{tabular} & CNN & 12 & 0.953 & 0.950 & 0.952 & 0.957 \\ \hline [PERSON] et al. [38] & QRS complexes & MLP & 1 & 0.899 & 0.892 & 0.895 & 0.907 \\ \hline [PERSON] et al. [39] & C-VAE & MLP & 1 & 0.90 & 0.84 & 0.86 & 0.84 \\ \hline [PERSON] et al. [40] & Gray-level matrix & XGBoost & 1 & 0.900 & 0.892 & 0.870 & 0.905 \\ \hline Proposed work & DA-BSM & CNN & 1 & 0.931 & 0.930 & 0.930 & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of the proposed DA-BSM method and state-of-the-art methods on arrhythmia classification using the SPH database (4-class classification: SR, AFIB, GSVT, SB; number of samples: SR = 2225, AFIB = 2225, GSVT = 2307, SB = 3889). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Pretrained** & **WSL** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Coarse-only & & & 0.9093 & 0.8458 & 0.8743 & 0.9655 \\ \hline Pretraining-only & & & 0.9085 & 0.9123 & 0.9104 & 0.9733 \\ \hline DA-BSM & & & **0.9215** & **0.9320** & **0.9267** & **0.9780** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification performance of the DA-BSM method on the PTB-XL database (SR vs. AFIB; number of samples: SR = 16,782, AFIB = 1514). To further validate the efficacy of the DA-BSM framework, a comparative analysis was conducted with existing state-of-the-art ECG arrhythmia classification methods on the PTB-XL database. Table 9 presents the key performance metrics, highlighting the advantages of the DA-BSM method in terms of accuracy and generalization performance compared to recent approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Pretrained** & **WSL** & **SR** & **AFIB** \\ \hline Coarse-only & & \(\blacktriangledown\) & 0.9813 & 0.7972 \\ \hline Pretraining-only & \(\blacktriangledown\) & & 0.9855 & 0.8353 \\ \hline DA-BSM & \(\blacktriangledown\) & \(\blacktriangledown\) & **0.9880** & **0.8653** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: F1-scores for classification of specific arrhythmia rhythm types in PTB-XL (SR vs. AFIB; number of samples: SR = 16,782, AFIB = 1514). Figure 6: t-SNE visualization of ECG embedding distributions for four arrhythmia classes at different training stages on the SPH database. In the study by [PERSON] et al. [42], ECG signals were converted into image representations in both the time and frequency domains separately for each lead. Although this approach achieved a high F1-score of 0.960, the requirement of all 12 ECG leads and the extensive data resampling to handle class imbalances limit its applicability in real-world clinical environments. [PERSON] et al.