## 5 Discussion
### Comparison of Results with the State-of-the-Art Methods
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Work**} & **Method** & **Acc** & **Pre** & **Recall** & **Spe** & **F1** \\  & **\%** & **\%** & **\%** & **\%** & **\%** \\ \hline [PERSON] et al. (2004) [44] & Statistical & 83.88 & 45.57 & 66.00 & 96.05 & 59.74 \\ [PERSON], [PERSON] (2009) [48] & LDA + QDA & 91.47 & 69.89 & 89.98 & 94.60 & 76.43 \\ [PERSON], [PERSON] (2011) [46] & QDA & 90.62 & 66.26 & 86.18 & 95.83 & 66.61 \\ [PERSON], [PERSON] (2014) [49] & LDA & 93.00 & 67.60 & 83.50 & - & - \\ [PERSON] et al. (2014) [61] & SVM & 86.66 & - & - & - & - \\ [PERSON] et al. (2017) [22] & ANN + SVM + PSO & 92.38 & 70.12 & 81.10 & 92.12 & 74.59 \\ [PERSON] et al. (2021) [15] & Wavelt + CNN & 97.48 & 70.75 & 67.47 & 96.01 & 68.76 \\ [PERSON] et al. (2021) [23] & LDA & 80.58 & 59.37 & 84.84 & 94.35 & 60.77 \\ [PERSON] et al. (2022) [50] & NGN + GV & 86.12 & 76.72 & 77.04 & 92.87 & 76.88 \\ [PERSON] et al. (2023) [51] & Self-ONN & 98.19 & 93.25 & 91.35 & 96.37 & 92.27 \\ CNN-AM-MLII-F1 & & 97.29 & 74.41 & 78.33 & 95.42 & 75.96 \\ CNN-AM-MLII-S & & 83.89 & 59.51 & 84.86 & 93.24 & 61.86 \\ CNN-AM-MLII + V-F1 & & 98.48 & 94.15 & 80.23 & 96.34 & 81.91 \\ CNN-AM-MLII + V-S & & 95.47 & 72.28 & 87.21 & 96.62 & 75.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of results for the interpatient paradigm—global indicators. effectively explore the accuracy improvement of the S class, whereas others, such as those by [22; 50; 61], focus more on general indicators. The study by [51] is noteworthy in this context because it advances methodologies for segmentation and the extraction of features in 1D data, revealing characteristics that have become decisive in regard to presenting one of the best current metrics for this paradigm, with a 92.27% overall F1 score and 83.3% and 83.5% accuracy and precision for the S class, respectively, without significantly reducing the sensitivity of the N class. Our results indicate that the proposed CNN-AM, especially for the two-lead scenario with a higher F1 score (MLII + V-F1), achieves the highest overall accuracy and precision, with values of 98.48% and 94.15%, respectively, and a high F1 score of 81.91%. In the scenario with one derivation and a higher F1 score (MLII-F1), the results are close to the values of correlated studies, with better performance in terms of the general indicators, precision and F1 score, demonstrating good model performance. With respect to the S class and the difficulties in its classification, a strong fluctuation in the values of the metrics obtained in all the studies can be observed, and our model with one derivation yielded a poor precision of 7.5% for this class, even with 77.5% accuracy. The performance of the S class was superior for only two leads, with accuracies of 43.3% and 56.3% in the scenario focusing on the highest overall F1 score and 69.3% and 20.2% for the scenario focusing on the greater sensitivity of the S class. In this context, the model with two leads and a higher F1 score becomes the most competitive in relation to the results presented in the comparison studies. In the case of the intrapatient paradigm, most studies in the literature only present results regarding the global indicators; thus, Table 14 was prepared. This table includes all related studies that used the MIT-BIH database but with a division of the different training and test databases, making a direct comparative analysis of the results impossible. Table 14 indicates that the intrapatient paradigm is promising, with competitive results for all the metrics, especially the results obtained for the proposed classifier with two derivations.