# Electrocardiographic Fragmented Activity (II):
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Data Base** & **Classifier** & **Signal Selection** & **Input Space** & **Sen** & **Spe** & **PPV** & **NPV** & **Acc** \\ \hline \multirow{2}{*}{**Sfrag**} & Linear \(\
u\)-SVM & Normalized QRS & Stats + PCA & 0.730 & 0.772 & 0.780 & 0.721 & 0.750 \\  & **NB** & **Non-normalized QRS** & **Stats + PCA** & **0.778** & **0.965** & **0.961** & **0.797** & **0.867** \\ \hline \multirow{2}{*}{**SWfrag**} & Linear \(\
u\)-SVM & Normalized QRS & Stats + PCA & 0.698 & 0.860 & 0.846 & 0.721 & 0.775 \\  & **NB** & **Non-normalized QRS** & **Stats + PCA** & **0.762** & **0.895** & **0.889** & **0.773** & **0.825** \\ \hline \multirow{2}{*}{**FHCM**} & Linear \(\
u\)-SVM & Normalized Beat & PowSum + RegPCA & 0.733 & 0.941 & 0.917 & 0.800 & 0.844 \\  & **RBF CSVM** & **Normalized QRS** & **Sum + 8 Ld** & **0.941** & **0.875** & **0.889** & **0.933** & **0.909** \\ \hline \multirow{2}{*}{**HCM**} & Linear \(\
u\)-SVM & Non-normalized QRS & Stats + 12 Ld & 0.649 & 0.714 & 0.673 & 0.692 & 0.683 \\  & **NB** & **Non-normalized QRS** & **Stats + 8 Ld** & **0.474** & **0.905** & **0.818** & **0.655** & **0.700** \\ \hline \end{tabular}
\end{table} 
Table 5: Summarized results for the best linear and no linear classifier for each database. The third used database was _Fhcm-DB_, which contains the real fragmented records. The results showed a much better figures than the subrogated models. This can be due the wide extension of the free parameters set in the synthetic model, appears to be too extensive compared to the effective real case. The best method was for this case then the C-SVM with Gaussian kernel over the aggregation of the normalized QRS from the eight independent leads. Final figures were 0.94 Sen, 0.88 Spe, 0.89 PPV, 0.93 NPV, and 0.91 Acc. The obtained values are very much comparable positively with results published in literature, improving slightly all existing reported results. Those ones proved a non-linear dependency among the selected features and the presence of fragmentation in the records. A new and challenging situation was found for our team when it was proposed by the clinicians to evaluate this very same techniques over the _HCM-DB_, which contains records from patients affected by fibrosis. Where as mentioned earlier in this paper, very little references are found in the scientific processing literature in this area. This lack of references is justified due to fact that these affections are rarely visible in the ECG, and diagnosis always require from an MRI-LGE. Though, academic researchers do not set their target on this affection when working with ECG processing. We hypothesis in this work, which although it might not be visible in the ECG, electrophysiological components related to the misconducting cells, could eventually be present inside the signal. The provided results are relevant, not because of the merit figures obtained, which are much lower than in the case of fragmentation, but unique as a clinical reference for better diagnosis. The best proposed method is compounded by NB classifier applied over the statistics computed from the real QRS of the eighth independent leads, and the merit figures obtained were 0.47 Sen, 0.91 Spe, 0.82 PPV, 0.66 NPV, and 0.70 Acc. As reader can see, even the results were not that good in terms of Sen, the presented algorithm achieved a high value of Spe. This is especially relevant, as nowadays does not exist any algorithm that allow the clinicians to evaluate the presence of fibrosis based on the ECG. As a general conclusion we can state that the algorithms and techniques presented in this paper opens a wide range of possible applications, such as, the development risk assessment tool for early diagnosis of misconducting affections namely, fibrosis, fragmentation, and so on. We also think that this paper opens a new opportunity in the ECG processing field to analyze and eventually develop improved algorithms for fibrosis detection to enhance the Sen presented in this work.