# Electrocardiographic Fragmented Activity (II):
## 4 Results
### Fibrosis Detection Based on Linear Models and Statistical Relevance  
In this subsection, we cover the results of the experiments applied over HCM-DB. As mentioned previously, and according to clinical criteria, it is not possible to visually identify this infection in ECG, and MRI-LGE is currently required for diagnosis. Hence, given that fibrosis is physiopathology related to missconduction in the heart, we tried to apply the same algorithmic. The scheme of the tested methods was next. A linear classifier was first selected from both SVM algorithms; Then, the interesting ECG segment was selected from the following ones, normalized beat, normalized QRS, non-normalized beat, and non-normalized QRS; Finally, the input space of the classifiers was computed according to Section 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Input Space** & **Signal Selection** & **Sen** & **Spe** & **PPV** & **NPV** & **Acc** \\ \hline NuSVM & Statistics + 3 PCA & Non-normalized Beat & 0.70 & 0.75 & 0.76 & 0.69 & 0.73 \\ NuSVM & Statistics + 3 PCA & Normalized Beat & 0.70 & 0.81 & 0.80 & 0.71 & 0.75 \\
**NuSVM** & **Statistics + 3 PCA** & **Non-normalized QRS** & **0.76** & **0.88** & **0.87** & **0.77** & **0.82** \\
**NuSVM** & **Statistics + PCA** & **Normalized QRS** & **0.75** & **0.90** & **0.89** & **0.76** & **0.82** \\ CSVM & Statistics + ICA & Non-normalized Beat & 0.78 & 0.63 & 0.70 & 0.72 & 0.71 \\ CSVM & Statistics + PCA & Normalized Beat & 0.65 & 0.88 & 0.85 & 0.69 & 0.76 \\ CSVM & Statistics + ICA & Non-normalized QRS & 0.76 & 0.79 & 0.80 & 0.75 & 0.78 \\ CSVM & Statistics + PCA & Normalized QRS & 0.70 & 0.86 & 0.85 & 0.72 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of linear classifiers over SWfrag-DB. Figure 7: Accuracy of Linear SVM applied over HCM-DB. (See Figure 5 for details) (**a**) \(\
u\)-SVM implementation. (**b**) C-SVM implementation. Figure 7a shows the Acc for \(\
u\)-SVM applied over HCM-DB and using each signal selection. The best result for this database is achieved using the statistics computed over the QRS taken from 12 leads as input for SVM, reaching \(Acc=0.68\), see Panel (2,1). Figure 7b shows the Acc for C-SVM applied over HCM-DB and using each signal selection. As can be observed, the best results for this database are achieved using as features of SVM the concatenation of non-normalized QRS from the 8 independent leads achieving \(Acc=0.683\), see Panel (2,3). Table 2 shows the combinations that achieved the best results for each signal selection and classifier. as can be observed, the minimum Acc for combinations was this time much lower, reaching only 0.64. The maximum Acc was 0.68, achieved by \(\
u\)-SVM, fed with the statistics computed over the non-normalized QRS of the 12 leads, and the concatenation the non-normalized QRS of the 8 independent leads. The use of non-normalized QRS exhibit a good performance, but the results, although much lower in the fragmentation analysis. These results are relevant because they proved the existence of fibrosis markers in the standard ECG. The goal of the second part of this experiment, is to describe the statistical relevance of the features of the best fragmentation detection methods described in previous experiment. Hence, we performed a bootstrap analysis with \(B\) equal to 100, after which we extracted the relevant features by selecting those ones the confidence interval of whose weight did not overlap zero. Figure 8 shows the confidence interval of the used features in the linear model that exhibit the best Acc applied over the HCM-DB. In this case, we can observe that not exist any non-overlapping-zero feature, hence we can say that any computed feature is statistically significant in our linear model. Which indicates that the relation among used features and fibrosis is not linear. Therefore, we need to explore the use of non-linear classifiers to enhance the fibrosis detection.