## 5 Discussion
### CNN-MA Component Analysis: Ablation Study  
To evaluate the contributions of certain individual components within the proposed CNN-AM structure, we conducted a study in which new classifiers were trained by removing certain model components. Tables 15-18 present the global interpatient metrics, featuring metrics by class for the interpatient and intrapatient approaches, respectively, of each new model developed and our CNN-AM for comparison, considering the obtained \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Work**}} & \multirow{2}{*}{**Method**} & **Acc** & **Pre** & **Recall** & **Spec** & **F1** \\  & & **\%** & **\%** & **\%** & **\%** & **\%** \\ \hline [PERSON] et al. (2018) [62] & CNN & 95.90 & 95.20 & 95.10 & - & - \\ [PERSON] et al. (2019) [3] & CNN & 92.96 & 90.08 & 80.08 & 98.14 & 82.17 \\ [PERSON] et al. (2019) [63] & CNN & 99.00 & - & - & - & - \\ [PERSON], [PERSON] (2018) [64] & CNN & 96.05 & 65.91 & 72.06 & 97.83 & 68.06 \\ [PERSON] et al. (2020) [65] & CNN & 98.35 & 82.24 & 93.82 & 99.01 & 87.29 \\ [PERSON] et al. (2020) [66] & SVM + RF + CNN & 95.90 & 96.34 & 95.90 & - & 95.92 \\ [PERSON] et al. (2020) [67] & DELM-LRF-BLSTM & 99.32 & 98.30 & 97.15 & - & 97.71 \\ [PERSON] et al. (2021) [24] & SVM + ANN & 98.29 & 99.22 & 98.29 & - & - \\ [PERSON] et al. (2021) [4] & CNN + SVM & 99.70 & 98.00 & 98.00 & - & - \\ [PERSON] et al. (2022) [68] & CNN & 95.20 & - & 95.20 & 98.80 & - \\ [PERSON] et al. (2023) [21] & ANN + CNN & 99.60 & 97.66 & 99.60 & - & 98.21 \\ [PERSON] (2023) [8] & CNN & 99.52 & 95.12 & 96.18 & - & 95.64 \\ [PERSON] (2024) [9] & CNN-FCA & 99.66 & 84.19 & 97.92 & 99.70 & 87.72 \\ CNN-AM MLII & & 99.59 & 97.17 & 95.97 & 99.04 & 96.56 \\ CNN-AM MLII + V & & 99.70 & 98.01 & 97.26 & 99.28 & 97.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Research results for the intrapatient paradigm—global indicators. metrics with the test database and with the MLII and MLII + V derivations. The models are as follows: \begin{tabular}{l l}
1. & AlexNet with ECG signal-to-image transformation with RP (M1); \\
2. & AlexNet with ECG signal-to-image transformation with the HSFC (M2); \\
3. & CNN-AM removing the attention module and for an MLII lead (M3); \\
4. & CNN-AM with the attention module and for an MLII lead (M4); \\
5. & CNN-AM removing the attention module and for two MLII + V leads (M5); \\
6. & CNN-AM with the attention module and for two leads MLII + V leads (M6). \\ \end{tabular}   
Considering the results for the interpatient and intrapatient cases, for the M1 and M2 models, which use only AlexNet with the HSFC or RP, the M4 models (MLII-F1) and M6 (MLII + V-F1) achieved superior global results, with improvements in all the metrics, indicating that the proposed structure, with the multimodal convolutional layer sequence \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **Acc** & **Pre** & **Recall** & **Spe** & **F1** \\ \hline M1 & MLII & 96.74 & 73.02 & 77.54 & 94.64 & 74.40 \\ \hline M2 & MLII & 97.14 & 73.42 & 76.28 & 94.92 & 74.69 \\ \hline M3 & MLII-f1 & 97.32 & 74.72 & 78.39 & 95.37 & 76.02 \\  & MLII-S & 85.32 & 62.39 & 83.98 & 93.43 & 64.01 \\ \hline M4 & MLII-F1 & 97.29 & 74.41 & 78.33 & 95.42 & 75.96 \\  & MLII-S & 83.89 & 59.51 & 84.86 & 93.24 & 61.86 \\ \hline M5 & MLII + V-F1 & 98.15 & 81.17 & 80.18 & 96.30 & 80.56 \\  & MLII + V-S & 92.79 & 63.84 & 69.17 & 91.9 & 62.70 \\ \hline
**M6** & **MLII + V-F1** & **98.48** & **94.15** & **80.23** & **96.34** & **81.91** \\  & MLII + V-S & 95.47 & 72.28 & 87.21 & 96.62 & 75.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Global interpatient indicators. and attention mechanism, can improve the results of the model based on conventional AlexNet despite the increase in computational complexity. For the models in which the attention mechanism was removed from the CNN-AM structure (models M3 and M5), for the cases of the interpatient approach and the MLII + V1 leads, the results achieved by CNN-AM were slightly higher.