## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Recording Place** & **SE** & **DET** & **AFCL** & **DF** \\ \hline LSPV & \(p=0.0006\) & \(p=0.0001\) & \(p>0.0500\) & \(p>0.0500\) \\ LIPV & \(p=0.0253\) & \(p=0.0080\) & \(p>0.0500\) & \(p=0.0287\) \\ RSPV & \(p<0.0001\) & \(p<0.0001\) & \(p=0.0192\) & \(p=0.0007\) \\ RIPV & \(p>0.0500\) & \(p>0.0500\) & \(p>0.0500\) & \(p=0.0035\) \\ ANT & \(p=0.0035\) & \(p=0.0016\) & \(p>0.0500\) & \(p=0.0387\) \\ POS & \(p=0.0002\) & \(p=0.0006\) & \(p>0.0500\) & \(p>0.0500\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mann–Whitney test _p_-values obtained, for each recording place, by averaging the _p_-values returned in the test performed for the three segment-length datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length**} & \multicolumn{2}{c}{**SE**} & \multicolumn{2}{c}{**DET**} \\ \cline{2-5}  & **ParAF** & **PerAF** & **ParAF** & **PerAF** \\ \hline
**1 s** & 0.123 \(\pm\) 0.042 & 0.148 \(\pm\) 0.057 & 0.608 \(\pm\) 0.135 & 0.524 \(\pm\) 0.160 \\
**2 s** & 0.120 \(\pm\) 0.043 & 0.145 \(\pm\) 0.056 & 0.656 \(\pm\) 0.140 & 0.556 \(\pm\) 0.177 \\
**4 s** & 0.117 \(\pm\) 0.043 & 0.143 \(\pm\) 0.057 & 0.688 \(\pm\) 0.135 & 0.585 \(\pm\) 0.179 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sample entropy and determinism mean and standard deviation values for datasets of 1, 2, and 4 s length segments. The values reported were obtained by averaging among CFAE. For the three datasets, the variables scoring provided by the Random Forest is presented in Figure 5. For the 1 and 2 s length datasets, the variables ranked with a score higher than 40 were DF\({}_{\text{LSPV}}\), DF\({}_{\text{RSPV}}\), DF\({}_{\text{RIPV}}\), SE\({}_{\text{LIPV}}\), and SE\({}_{\text{pos}}\), while in the 4 s length dataset, the AF\({}_{\text{ANT}}\) was also included as it surpassed the threshold value. After testing all the possible combinations of the high-ranked features, the group SE\({}_{\text{POS}}\), DF\({}_{\text{LSPV}}\), DF\({}_{\text{RSPV}}\), and DF\({}_{\text{RIPV}}\) provided the best classification performance to discriminate  
Figure 4: Correlation matrix showing the degree of bound between indices, applied to the considered recording places. The variable TYPE was assigned a value 0 if the patient is paroxysmal, and 1 if the patient is persistent AF. Figure 5: Variable importance ranked with the Random Forest algorithm for 1, 2, and 4 s length analyses. The cut-off value of 40 is shown in red. between the CFAE of ParAR and PerAF with an accuracy of 88.3% for all the segment lengths. The same accuracy was reached by also adding SE\({}_{\text{LISP}}\) to the group. However, in order to simplify the model as much as possible, it was decided to consider the minimal set of features as optimal. The accuracy values of the models built with the other possible combinations of the highest-ranked features (score \(>\) 40) had a mean and standard deviation of 70.7 \(\pm\) 8.7% for the 1 s length, 70.8 \(\pm\) 8.3% for the 2 s length, and 69.2 \(\pm\) 8.8% for the 4 s length, which is a significant reduction in the accuracy. Finally, the highest accuracy achieved by a single index was provided by DET\({}_{\text{LIFV}}\) with 82.2% for any segment length as well, while the averaged single accuracies reached by the other indices were 60.2 \(\pm\) 11% for the 1 s length, 54.7 \(\pm\) 13.7% for the 2 s length, and 57.3 \(\pm\) 11.4% for the 4 s length.