## 7 Results
### Results for Individual Classifiers  
In this first test, individual classifiers are used. The results are obtained using four different classification algorithms: \(L2 RLR\), \(ANNC\), \(BAGG\), and \(KNN\)[8; 31]. After several trials, the parameters for the classifiers were the following:  
* \(L2 RLR\): With regularization parameter \(\lambda=10^{-9}\). In this case, the \(\lambda\) value is very small to account for high values in regression coefficients. * \(ANNC\): Two hidden layers, 20 neurons in each hidden layer. Two layers allow better classification in case of a high number of inputs, as in this case. In case of a single layer, a higher number of neurons should be used. * \(BAGG\): 600 decision trees. This was an experimental value. A higher number of trees did not produce better results. * \(KNN\): Euclidean distance showed a good performance, together with K = 1. Tables 1 and 2 summarize the results achieved by making comparisons between the values of sensitivity, specificity, accuracy, and precision. When analyzing the values shown in the tables, it is observed that the \(KNN\) classifier obtained the best result, with a sensitivity of 94.97 \(\pm\) 0.70%, a global specificity of 99.27 \(\pm\) 0.05%, accuracy of 98.47 \(\pm\) 0.01%, and an overall precision of 97.09 \(\pm\) 0.14% achieved for \(VF\). For \(VT\), a sensitivity of 93.47 \(\pm\) 0.19%, specificity of 99.39 \(\pm\) 0.15%, accuracy 98.97 \(\pm\) 0.08%, and an overall precision of 92.11 \(\pm\) 0.7% was obtained. For a complete analysis, the confusion matrices for the classes and the used algorithms were calculated (Tables 3 and 4). These tables show that the main conflicts exist in pairs: \(VF\) and \(VT\), \(Normal\) and \(Others\). Actually, since the number of segments is lower for \(VF\) and \(VT\) compared to normal and Others, the proportion of confusion mainly resides in \(VF\) and \(VTS\), which was expected according to other algorithm results and clinical practice. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{_VF_} & \multicolumn{4}{c}{_VT_} \\ \cline{2-9}  & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** \\ \hline
**Classification Algorithms** & _VF_ & **Global** & **Global** & **Global** & **\(VT\)** & **Global** & **Global** & **Global** \\ \hline \(L2 RLR\) & & & & & & & & \\ Test & 91.54 \(\pm\) 0.46 & 98.45 \(\pm\) 0.06 & 97.15 \(\pm\) 0.09 & 94.74 \(\pm\) 1.27 & 88.15 \(\pm\) 0.59 & 98.81 \(\pm\) 0.04 & 98.07 \(\pm\) 0.07 & 99.67 \(\pm\) 1.77 \\ \hline \(ANNC\) & & & & & & & \\ Test & 95.56 \(\pm\) 0.33 & 98.80 \(\pm\) 0.13 & 98.19 \(\pm\) 0.15 & 95.26 \(\pm\) 0.45 & 88.80 \(\pm\) 1.13 & 99.52 \(\pm\) 0.06 & 98.87 \(\pm\) 0.03 & 94.05 \(\pm\) 0.80 \\ \hline \(BAG\) & & & & & & & \\ Test & 98.46 \(\pm\) 0.26 & 98.43 \(\pm\) 0.12 & 98.44 \(\pm\) 0.14 & 97.09 \(\pm\) 0.14 & 84.8 \(\pm\) 1.70 & 99.86 \(\pm\) 0.04 & 98.82 \(\pm\) 0.12 & 97.81 \(\pm\) 0.82 \\ \hline \(KNN\) & & & & & & & \\ Test & 94.97 \(\pm\) 0.70 & 99.27 \(\pm\) 0.05 & 98.47 \(\pm\) 0.01 & 97.09 \(\pm\) 0.14 & 93.47 \(\pm\) 0.19 & 99.39 \(\pm\) 0.15 & 98.97 \(\pm\) 0.08 & 92.11 \(\pm\) 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results for the different algorithms adaptive neural network (\(ANNC\)), Bagging (\(BAG\)), L2 Regularization (\(L2 RLR\)), and K-nearest neighbor (\(KNN\)) using as input the time-frequency representation image (\(TFRI\)) used to characterize and detect \(VF\) and \(VT\) classes.