## 7 Results
### Comparative Study for the Method of Combined Classifiers
[37] using SVM where Sens = 96.20% to \(VF\), Spe = 96.20%, and Acc = 96.3%, for a 2 s window, was obtained; in this case, a sensitive different set of source signals was used. Other works provides good performance ratios distinguishing between \(VF\) and \(VT\) when applied to compressed ECG signals [41]. In all cases, our performance results are slightly or sensitive better. As a second group of comparable works we can find those aiming to distinguish normal sinus (N) apart from \(VT\) or \(VF\). Within this group, [PERSON] et al. [42] obtained good accuracy ratios (Acc(\(VF\)) = 90.9%, Acc(\(VT\)) = 84.0%, Acc(N) = 100%) using a type-2 fuzzy logic-based classifier for a three class multiclass classification (\(VF\), \(VT\) and \(Normal\)). [PERSON] also described the results of using a SOM neural network with poor VT accuracy. Later, [PERSON] et al. [43] followed the same line implementing another multiclass classifier using a type-2 TSK fuzzy system, with the same three classes than [PERSON] used; in this case, with better accuracy ratios (Acc(\(VF\)) = 93.3%, Acc(\(VT\)) = 92.0%, Acc(N) = 100%). They also tried a a type-2 Mandami fuzzy system with lower values. Other works analyse a binary distinction between \(VF\) and non-\(VF\) rhythms. [PERSON] et al. [44] used 17 features: Morphological, spectral, and complexity. Here, the random forest classifier has been used for discrimination between \(VF\) category and non-\(VF\) category, with Acc = 94.79%, Sens = 95.04%, Spe = 94.78%. In [45], they used 13 parameters accounting for temporal (morphological), spectral, and complexity features of the ECG signal, using an SVM to distinguish between \(VF\) and non-\(VF\) categories with Sens = 95%, Spe = 99%. In another attempt [46], different heart rhythms were detected and classified into the \(VF\) and non-\(VF\) types using six features, four are derived from image-based phase plot analysis, one is derived in the frequency domain, and the last reflects the nonlinear characteristics of a data segment, values of (Acc = 95.3%, Sens = 94.5%, Spe = 94.2%) and (Acc = 90.4%, Sens = 91.6%, Spe = 89.3%) using binary decision tree (BDT) and the SVM, respectively. The algorithm proposed by [PERSON] et al. [47], using digital Taylor-Fourier transform (DTFT) features of ECG signals and least square support vector machine (LS-SVM) with linear and radial basis function (RBF) kernels for detection of \(VF\) and non-\(VF\) arrhythmia episodes, obtained performance values of Acc = 83.75%, Sens = 85.20%, Spe = 82.46%. Other authors have classified the ECG signal segments into \(VFVT\) and non-\(VFVT\). These results are not directly comparable with those in the previous table since they provide a binary output. However, we include them since they are interesting to see how simpler two-class classification still provides similar results to those obtained in this work. [PERSON] et al. [48] classified the ECG signal segments into the normal sinus rhythm (NSR) or arrhythmatic shockable classes \(VFVT\). The classification is based on Time-Delay Transform (TDT) of the signals and a neural network with Weight Fuzzy Membership Functions (NEWFM). They obtained Acc = 89.5%, Sens = 73.6%, and Spe = 93.5%. [PERSON] et al. [49] detected \(VFVT\) using boosted classification and regression tree (Boosted-CART) obtaining Acc = 98.29%, Sens = 97.32%, and Spe = 98.95%. Other studies that have also used the class \(VFVT\)[1] have evaluated both time domain (e.g., energy, permutation entropy) and frequency domain (e.g., renyientropy) features. The classification is done by using a Random Forest (RF) classifier aiming to identify shockable and non-shockable ventricular arrhythmia with CUDB and MITDB databases with results of Acc = 97.23%, Sens = 96.54%, Spe = 97.97% [50]. Thirteen time-frequency and statistical features were extracted and applied to the C4.5 classifier [51], resulting in Acc = 97.02%, Sens = 90.97%, Spe = 97.86% for \(VFVT\) detection (including ventricular flutter). In [PERSON] et al.