## 4 Experiments
### Experimental Results
#### 4.3.5 Comparison with State-of-Art Methods  
Table 12 shows the comparison between our model with other methods. We consider all the results from [3] and compared the results with our method. All results in the table are based on only the MITDB database, as it is widely used by other works, and also convenient to use for UDA tasks. For a fair comparison, we utilized several traditional methods [37; 41; 42; 43; 44; 45; 46] methods to compare results. These traditional methods are based on interpatient and patient-specific settings. For comparison purposes, we used DS1 \(\rightarrow\) DS2. These methods can be classified into interpatient, patient-specific, unsupervised domain adaptation, and source-free domain adaptation. In all settings, the results are considerably good in the ECG classification task. But the main challenge of generalization in unseen samples is still unfolding in traditional approaches. On the other hand, UDA methods can unfold this issue with its generalization capability in unseen domains. But it still lacks privacy in real-world settings. On the other hand, our proposed method SF-ECG outperformed the traditional-DL methods as well as UDA methods, even in critical new settings of UDA. Our method has many advantages, since it does not require a source set during training. So, it preserves privacy and generalization. This is considered a great advantage in many ECG classification applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Methods** & **Types** & \multicolumn{3}{c}{**N (\%)**} & \multicolumn{3}{c}{**V (\%)**} & \multicolumn{3}{c}{**S (\%)**} & \multicolumn{3}{c}{**F (\%)**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \hline  & **Se** & **Pp** & **F1** & **Se** & **Pp** & **F1** & **Se** & **Pp** & **F1** & **Se** & **Pp** & **F1** & \\ \hline
[37] & IP & 99.2 & 95.2 & 90.0 & 93.9 & 90.9 & 92.0 & 91.1 & 42.2 & 58.0 & - & - & - & 93.8 \\ \hline
[41] & IP & - & - & - & 91.25 & 88.3 & 90.0 & 62.7 & 61.2 & 62.0 & - & - & - & - \\ \hline
[42] & IP & 98.9 & 97.4 & 98.0 & 85.7 & 94.1 & 89.0 & 76.5 & 76.6 & 76.0 & 25.0 & 1.79 & 0.0 & 96.4 \\ \hline
[43] & IP & 88.5 & 98.8 & 93.0 & 92.0 & 72.1 & 80.0 & 82.0 & 30.4 & 44.0 & 68.3 & 26.6 & 38.0 & 95.3 \\ \hline
[44] & IP & 91.8 & 98.9 & 95.0 & 95.1 & 90.1 & 92.0 & **89.0** & 35.4 & 50.0 & 32.2 & 20.3 & 25.0 & 91.4 \\ \hline
[45] & PS & 97.6 & 98.5 & 98.0 & 93.8 & 92.4 & 93.0 & 76.8 & 74.0 & 75.0 & 79.6 & 62.4 & 70.0 & 96.1 \\ \hline
[46] & PS & 99.7 & 97.2 & 98.0 & 91.8 & **98.0** & 95.0 & 61.4 & **90.7** & 73.0 & 9.0 & 28.3 & 14.0 & 97.0 \\ \hline
[10] & UDA & - & - & - & - & - & - & - & - & - & - & - & - & 93.0 \\ \hline
[3] & UDA & 99.1 & 98.4 & 99.0 & 94.0 & 92.3 & 93.0 & 76.5 & 90.2 & 83.0 & 57.9 & 74.1 & 65.0 & 97.6 \\ \hline
[5] & UDA & 99.4 & 98.0 & 98.7 & 91.4 & 87.2 & 89.2 & 57 & 88.6 & 69.4 & 37.3 & 47.6 & 41.8 & 96.8 \\ \hline
**SF-ECG (Ours)** & **SFDA** & **99.4** & **99.2** & **99.7** & **96.4** & 97.2 & **98.2** & 87.1 & 88.6 & **89.4** & **81.7** & **86.9** & **82.8** & **98.4** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results on PTB \(\rightarrow\) PTB_XL, cross-patient classification task.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**MI-Sub Category** & **Sensitivity (\%)** & **Positive Predictivity (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline AMI & 73.5 & 70.3 & 69.3 \\ \hline ASMI & 88.3 & 89.2 & 92.3 \\ \hline IMI & 88.2 & 92.9 & 86.2 \\ \hline ALMI & 71.3 & 73.6 & 74.3 \\ \hline ALMI & 86.6 & 70.5 & 85.2 \\ \hline HC & 96.4 & 96.3 & 93.6 \\ \hline Average & 85.7 & 82.1 & 83.4 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison between recent state of the art with our source-free adaptation method and unsupervised domain adaptation method on MITDB DS2. All the results are taken from [3]. Here, we denote interpatient = IP, patient-specific = PS, unsupervised domain adaptation = UDA, and source-free domain adaptation = SFDA.