# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 3 Results
Using a significance level of 5% (\(p\)-value) to test the null hypothesis of no correlation against the alternative hypothesis of a nonzero correlation, the results indicate no significant evidence of the correlation between the following pairs: data--amount and accuracy--spikes, data--amount and all other criteria, accuracy--PLI and completeness, accuracy--PLI and loss of samples, accuracy--PLI and PLI--real, accuracy--PLI and spikes--real, and accuracy--spikes and loss of samples--real. For the remaining criteria, the results provide significant evidence supporting the absence of correlation. For level 1, four quality criteria (i.e., consistency, accuracy, precision, and reputation) were calculated from 110 maps (i.e., ApEn, mean, STD, SampEn, ShEn). The metrics applied for each criterion are described in Table 2. The correlations among level 1 quality criteria are listed in Table 4. There are no high correlations among quality criteria, and considering a significance level of 5% to test the null hypothesis of no correlation against the alternative hypothesis of a nonzero correlation, there is no significant evidence of a lack  
Figure 11: Statistical feature maps affected by different levels of perturbations of loss of samples/spikes/PLI, with (**a**) loss of resolution of 0%, and (**b**) loss of resolution of 97.3%. The color scale depicts the normalized values of statistics estimations. of correlation between reputation and consistency, while there is significant evidence of a lack of correlation for the other criteria. To determine the effects of IQ among levels, correlations were calculated between IQ inputs and IQ outputs. Table 5 presents the correlations between pairs of IQ inputs (where outputs of level 0 serve as inputs of level 1) and IQ outputs of level 1. There are no high correlations among quality criteria. Considering a significance level of 5%, there is significant evidence of a lack of correlation between consistency and accuracy--PLI, precision and loss of sample, and precision and PLI. For reputation, there is no significant evidence of a lack of correlation between accuracy--spikes and spikes. The accuracy of level 1 shows significant evidence of a lack of correlation with all variables of level 0. Since accuracy shows no correlation, a dependency analysis was performed using the Reliefief algorithm with 11 nearest neighbors [47]. Table 6 summarizes the correlations between pairs of IQ inputs (outputs of level 1) and IQ outputs of level 2. Objectivity is highly correlated with interpretability, reputation of input with reputation of output, consistency with objectivity, and reputation of output with accuracy of input. These correlations highlight the most relevant IQ factors affecting data processing. Similarly, Table 7 shows correlations among the IQ inputs (outputs of level 2) and IQ outputs of level 3. There is a high correlation between accuracy and interpretability and between the objectivity of input and output. Efficiency shows no correlations with any IQ criteria except for interpretability at level 2. To maximize the quality of the IQ results at each level, support vector regression was applied to model IQ by level, using 80% of the data for cross-validation and 20% for testing. The performance of these models is shown in Tables 8-10 in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). \begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline
**Noise** & **Consistency** & **Precision** & **Reputation** & **Accuracy** \\ \hline
**Consistency** & 1 & 0.293 & 0.187 & 0.427 \\
**Precision** & 0.293 & 1 & 0.286 & 0.552 \\
**Reputation** & 0.187 & 0.286 & 1 & 0.487 \\
**Accuracy** & 0.427 & 0.552 & 0.487 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation between pairs of IQ criteria at level 1.