## 5 Discussion
[68] obtained an accuracy of 88.60% using chaotic-based reconstructed phase space features to detect VF episodes. [PERSON] et al. [69] achieved a high value in the specificity of detecting VF episodes (Sp = 98.51%) using an improved version of the Threshold Crossing Interval (TCI) algorithm, called TCSC, and the MIT-BIH and CUDB databases. However, this detection was carried out with a sensitivity as low as 80.97%. Later, [PERSON] et al. [70] obtained high values of specificity and accuracy (Spe = 97.10% and Acc = 96.80%) for the discrimination of VF episodes, with their specific feature selection and SVM classifiers. In their case, the sensitivity got a moderate value of 91.90%. Further, [PERSON] and [PERSON] [71] used a genetic algorithm to make the feature selection for classifying VF episodes, achieving high-performance values: Sen = 98.40%, Spe = 98.00%, and Acc = 96.30%. Next, [PERSON] et al. [72] obtained high-performance values of specificity (Spe = 98.19%) and accuracy (Acc = 97.88%) using a Convolutional Neural Network (CNN) for the detection of VF. However, they achieved an extremely low value of sensitivity (Sen = 56.44%). Finally, in 2019 [PERSON] et al. [73] got the highest results in this group, using a scheme of incorporating Empirical Mode Decomposition (EMD) and SVM classifiers (Sen = 99.99%, Spe = 98.40%, Acc = 99.19%) for the classification of VF and non-VF episodes. As it can be seen, the results of the TDA proposal in this work achieve one of the best results (Sen = 97.07%, Spe = 99.25%, and Acc = 98.68%) compared with other works of the VF-discriminating group, with the only exception of [PERSON] [73], that obtained better results. However, to establish a fair comparison, note that [PERSON] obtained slightly higher results (i.e., a difference of 0.51% in Accuracy) at the expense of preselecting and rejecting the noise episodes, while in this work, there was not any preselection of ECG episodes. Furthermore, another group of works in the bibliography can be compared, distinguishing between VT and VF rhythms (Table 11). In this group, [PERSON] et al. [74] proposed a fuzzy similarity-based approximate entropy approach, distinguishing between VT and VF and obtaining high-performance ratios (Sen = 97.98% and Spe = 97.03% to VF and Sen = 97.03% and Spe = 97.98% to VT). However, to establish a fair comparison, it must be considered that Xie was selected as input data representative and clean episodes of VF and VT, while our work was done without preselection of ECG episodes. This kind of preselection is usual in the literature, as in [PERSON] and [PERSON] [75], that used a selection of VF and VT episodes from the MIT-BIH database, using Empirical Mode Decomposition (EMD) and Approximate Entropy. [PERSON] and [PERSON] obtaining moderate values for classification performance (Sen = 90.47%, Spe = 91.66%, and Acc = 91.17%). Later, [PERSON] et al. [76] obtained high performance values (Sen = 98.15% and Spe = 96.01% to VF, and Sen = 96.01% and Spe = 98.15% to VT) using Lempel-Ziv and Empirical Mode Decomposition (EMD). In this case, a selection of clean episodes of VT and VF was made too. Finally, the authors of the present work achieved high values of classification performance [47] feeding the complete time-frequency image as the input of different classifiers (e.g., Sen = 92.8% and Spe = 97.0% to VF and Sen = 91.8% and Spe = 98.7% to VF, using an Artificial Neural Network Classifier, ANNC). In any case, the results of the TDA method in this work achieve the best results when compared with the rest of the works in the bibliography aiming to discriminate between VF and VT rhythms (despite the preselection of ECG episodes done by some works). Table 10 shows a comparison focused on detecting VT/VF episodes, i.e., shockable and non-shockable. This set of works usually targets its implementation on external defibrillators (AED) and implantable cardioverter defibrillators (ICD). Thus, these works distinguish between shockable and non-shockable rhythms (considering shockable both VT and VF).