## 7 Results
### Comparative Study for the Method of Combined Classifiers
Although different works are roughly comparable, we set two different groups for better comparison: those works aiming to distinguish between \(VF\) and \(VT\), and those works classifying multiple rhythms. Figure 9: Classification rate for sensibility to each class, for the test dataset using z-5 and z-9 cross validation. Individual and combined VMM and HM classifiers are shown. \begin{table}
\begin{tabular}{l r r r r r r r r r r r r r r r} \hline
**Types** & & \multicolumn{3}{c}{**\(VF\)**} & \multicolumn{3}{c}{\(VT\)} & \multicolumn{3}{c}{_Other_} & \multicolumn{3}{c}{_Normal_} & \multicolumn{3}{c}{**Data Base**} \\ \hline
**Method** & **Sens \%** & **Spe \%** & **Acc \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Sens \%** & **Spe \%** & **Acc \%** \\ \hline This work, ANNC(t-f) & 95.56 & 98.80 & 98.19 & 88.80 & 99.52 & 98.87 & 96.86 & 99.55 & 99.57 & 98.98 & 97.72 & 98.40 & AHA \& MIT-BHH \\ This work, BAGC(t-f) & 98.46 & 98.43 & 98.44 & 84.58 & 99.96 & 98.82 & 95.66 & 99.87 & 98.95 & 99.39 & 97.61 & 98.57 & AHA \& MIT-BHH \\ This work,ANN(t-g) & 94.97 & 99.27 & 98.47 & 93.47 & 99.39 & 98.97 & 97.69 & 99.40 & 99.03 & 99.07 & 98.29 & 98.71 & AHA \& MIT-BHH \\ This work,BAGC\_KNN(t-f) & 95.58 & 99.31 & 98.6 & 94.02 & 99.31 & 99.14 & 97.42 & 99.52 & 99.07 & 99.02 & 98.36 & 98.72 & AHA \& MIT-BHH \\
[3], single Boltzmann & 92.52 & & & & & & & & & & & & & \\ \hline L using Discriminant Analysis & 94.10 & 93.80 & & & & & & & & & & & & \\ L using filter and counts & 94.40 & 95.90 & 94.7 & & & & & & & & & & & \\  & & 96.20 & 96.20 & 96.3 & & & & & & & & & & \\  & & 98.15 & 96.01 & 97.1 & & & & & & & & & & & \\  & & 99.15 & 97.03 & 97.5 & & & & & & & & & & & \\  & & 99.18 & 90.21 & 90.1 & & & & & & & & & & & \\  & & 99.18 & 90.21 & 90.1 & & & & & & & & & & & \\  & & 90.47 & 91.66 & 91.2 & & & & & & & & & & & \\  & & 98.10 & 88.00 & 93.2 & & & & & & & & & & & \\  & & 99.15 & 99.01 & 91.3 & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & \\  & & 99.15 & 99.01 & 91.3 & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & \\  & & 99.15 & 99.01 & 91.3 & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & & \\  & & 99.0 & 84.0 & & & & & & & & & & & & \\  & & 99.15 & 99.01 & 91.3 & & & & & & & & & & & \\  & & 99.0 & 84.0 & & &For the first group, [PERSON] et al. [39] used approximate entropy to distinguish between \(VF\) and \(VT\) with performance ratios of Sens = 91.84% to \(VF\), Spe = 90.2%, Acc = 91.0%, using similar signal sources than our work. In addition, they also proposed a modified version using fuzzy similarity-based approximate entropy that, in turn, got high performance ratios (Sens = 97.98% to \(VF\), Spe = 97.03%, Acc = 97.5%). Although we obtained higher values, to make a fair comparison between both analysis, it has to be taken into account that [PERSON] used representative and clean episodes of \(VT\) and \(VF\) as input data, in front of our work that used a multiclass scheme, classifying four types of rhythms and considering complete patient's registers as the input signal. The same happens for other studies distinguishing between \(VF\) and \(VT\) rhythms; [PERSON] and [PERSON] [40] used approximate entropy with Empirical Mode Decomposition (EMD) and a more reduced dataset than [PERSON], having good performance values (Sens = 90.47 to \(VF\), Spe = 91.66%, Acc = 91.2%). Later, [PERSON] et al. [38] also used, in the same line, Lempel-Ziv Complexity and EMD in the same conditions that [PERSON] did before, using a representative number of clean episodes of each pathology, and they also got high performance ratios (Sens = 98.15% to \(VF\), Spe = 96.01%, Acc = 97.1%). The same occurred to [PERSON] et al.