## 5 Discussion
In this group, [PERSON] et al. [71] achieved an Accuracy of Acc = 98.1% (Sen = 98.4% and Spe = 98.0%) using a Genetic Algorithm (GA) for feature selection and a SVM classifier. The same year, [PERSON] et al. [70] also achieved high classification performance values (Acc = 98.6, Sen = 95.0%, and Spe = 99.0%) using a selection of features and a Support Vector Machine (SVM) classifier. This work obtained one of the highest accuracy and specificity  
values in this group. In 2016, [PERSON] et al. (2018) used the Variational Mode Decomposition (VMD) and the Random Forest (RF) classifier to detect and classify shockable and non-shockable ECG episodes, achieving high values of accuracy, sensitivity, and specificity of 97.23%, 96.54%, and 97.97%, respectively. Later, in 2018, [PERSON] et al. (2018) detected and classified ventricular arrhythmias using cubic support vector machine (SVM) and C4.5 classifiers and achieving an Accuracy of Acc = 97.02% (Sen = 90.97% and Spe = 97.86%). [PERSON] et al. (2017) brought forward an eleven-layer Convolutional Neural Network (CNN) for the classification of shockable and non-shockable arrhythmias. They obtained a 93.18% accuracy (Sen = 91.04% and Spe = 95.32%). Finally, [PERSON] et al. (2018) detected and classified ventricular arrhythmia using cubic support vector machine (SVM) and C4.5 classifiers, achieving high accuracy of Acc = 97.02% (Sen = 90.97% and Spe = 97.86%). As it can be seen, the results of the TDA proposal in this work show the highest performance values also in this group of works, achieving an accuracy of 99.51%, 99.03% sensitivity, and 99.67% specificity. Thus, the benefits of using the geometric features extracted from Topological Data Analysis (TDA) in the classification procedure are clear. Then, we can state that TDA, and the geometric features derived from it, can be successfully used both in the detection and classification of ventricular arrhythmias and in the classification of shockable episodes. It proves that the geometric features derived from Topological Data Analysis provides a good description of the signal. Moreover, it also foresees a successful application of these features in both Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies. It should be taken into account that these good results occur even in the absence of preselected ECG episodes. This work performs data classification in the same form as an Automated External Defibrillator (AED) operating in an emergency situation, following the AHA recommendations for Automated External Defibrillator (AED) algorithm performance (79). That is, data can be continuously analyzed in time windows as they are received from the electrocardiogram. To conclude, the success of using the TDA-derived geometric features suggests that this method may overcome conventional feature selection limitations by better describing the'shape of data' and, thus, enabling us to build better performance arrhythmia detectors. \begin{table} \begin{tabular}{l c c c c} \hline \hline
**Types** & \multicolumn{4}{c}{**Shockable (VT+VF)**} & \multicolumn{1}{c}{**Data Base**} \\ \hline
**Method:** & **Sens\%** & **Spe\%** & **Accu\%** & **Data Base** \\ \hline This work, TDA & 99.03 & 99.67 & 99.51 & \(AHA,MITBIH\) \\ \hline This work, PDI & 89.63 & 96.96 & 95.12 & \(AHA,MITBIH\) \\ \hline
[77] Convolutional neural network (CNN) (2018) & 91.04 & 95.32 & 93.18 & \(CUDB,MITBIH\) \\ \hline
[11] C4.5 classifier (2018) & 90.97 & 97.86 & 97.02 & \(CUDB,MITBIH\) \\ \hline
[78] Adaptive variational and boosted CART (2018) & 97.32 & 98.95 & 98.29 & \(CUDB,MITBIH\) \\ \hline
[71] SVM and bootstrap (2013) & 98.40 & 98.00 & 98.10 & \(AHA,CUDB,MITBIH\) \\ \hline
[9] VMD with Random Forest (2016) & 96.54 & 97.97 & 97.23 & \(CUDB,MITBIH\) \\ \hline
[70] FS and SVM (2013) & 95.00 & 99.00 & 98.60 & \(CUDB,MITBIH\) \\ \hline \hline \end{tabular}