## 4 Experiments and Results
### PCA as Fragmentation-Wave Enhancement Tool
As we can see, the parameters with the strongest effect over the dispersion coefficient are the amplitude of the fragmented wave and its width. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Frag 1** & **Frag 2** & **Frag 3** & **Frag 4** & **Frag 5** & **Frag 6** & **Frag 7** & **Frag 8** \\ \hline ctrl 1 & 1 & 0 & 0 & 0 & 0 & **0** & **0** & 0 \\ ctrl 2 & 0 & 0.995 & 0.002 & 0 & **0** & **0** & **0** & 0.002 \\ ctrl 3 & 0 & 0.005 & 0.988 & 0.005 & **0** & **0.002** & **0** & 0 \\ ctrl 4 & 0 & 0 & 0.007 & 0.959 & **0.022** & **0.01** & **0** & 0.002 \\ ctrl 5 & 0 & 0 & 0.002 & 0.036 & **0.887** & **0.048** & **0.024** & 0.002 \\ ctrl 6 & 0 & 0 & 0 & 0 & **0.089** & **0.824** & **0.077** & 0.01 \\ ctrl 7 & 0 & 0 & 0 & 0 & **0** & **0.113** & **0.853** & 0.034 \\ ctrl 8 & 0 & 0 & 0 & 0 & **0.002** & **0.002** & **0.046** & 0.949 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Normalized confusion matrix of PCA for the 8 independent leads from ECG. The _ij_ value of the table represent the ratio of _i_-th component of control patient that is paired with the _j_-th component of synthetically fragmented patient according to the value of PCC. The next experiment was performed to evaluate the effect of the _fragmentation wave parameters variation over the \(\rho_{RMSE}\) coefficient_. For this experiment, we used the same values of the previous ones and computed the \(\rho_{RMSE}\) coefficient for each parameter and for each principal component. As a result, Figure 6a shows the effects of fragmented wave amplitude variation on the \(\rho_{RMSE}\) coefficient, Figure 6b shows the effects of fragmented wave width on the \(\rho_{RMSE}\) coefficient, Figure 6c shows the effects of the number of cycles included in the fragmented wave on the \(\rho_{RMSE}\) coefficient, Figure 6d shows the effect of the variation on the number of leads affected. As can be seen in Figure 6a,b, the \(\rho_{RMSE}\) coefficient distribution is positive in the majority of the principal components. This effect is due to the emergence of new components caused by large amplitude or width that PCA tends to isolate them. As it can be seen in Figure 6c,d, the \(\rho_{RMSE}\) distribution is negative in the higher (detail) components and it becomes positive when we go to the lower (baseline) components. According to literature and knowledge, and as an expected behavior, this is due to the fact that these two parameters are not solid enough to create new components. Figure 4: Measurements of fragmentation enhancement using PCA over independent leads. (**a**) Distribution of \(\rho_{RMSE}\) coefficient for each component, values higher than 0 represent fragmented wave enhancement. (**b**) Enhancement ratio for each component. (**c**) Joint enhancement ratio. The last set of PCA experiments was created to try out the _enhancement in fragmentation detection through heart-region projections_. According wto medical knowledge, the ECG leads can be divided in three  
Figure 5: Dispersion coefficient against the fragmented wave amplitude (**upper left**), fragmented wave width (**upper right**), fragmented wave number of cycles (**lower left**), and number of affect lead (**lower right**) for independent-lead PCA model. Figure 6: Effect of the different parameters of the subrogated fragmented wave over the \(\rho_{RMSE}\) coefficient for independent-lead PCA model. Values lower than 0 correspond with RMSE higher in the ECG than in the principal components, values higher than 0 correspond with RMSE higher in principal components than in the ECG. (**a**) Effect of the fragmented wave amplitude, (**b**) effect of the fragmented wave width, (**c**) effect of the cycle number of fragmented waves, and (**d**) effect of the number of affected leads. main groups related to the heart region that they reflect. These regions are: utero-septal, which includes V1, V2, V3 and V4; Lateral, which includes I, aVL, V5 and V6; and inferior, which includes II, III and aVF.