###### Abstract
[7] used the Wavelet method to detect ECG arrhythmias with three types of episodes (Normal, VT, and VF). In [8], [PERSON] et al. used an SVM with Gaussian Kernel to detect ventricular abnormalities with morphological features. [PERSON] et al. [9] detected and classified shockable (VF/VT) arrhythmias using Variational Mode Decomposition with Random Forest (RF) decision trees. In [10], [PERSON] et al. used fixed thresholds to implement a real-time detection of shockable episodes (VF/VT). In addition, in the same manner, other works harnessed other machine learning techniques for the detection and recognition of ventricular arrhythmias, as in [PERSON] et al. [11], who used a C4.5 classifier; [PERSON] et al. [12], who employed a k-Nearest Neighbor (kNN) classifier; [PERSON] et al. [13], who used Bayesian decision; or [PERSON] et al. [14], who employed Independent Component Analysis (ICA) and Decision Trees. Over the last few years, there has been a general surge in the use of algebraic topology to analyze statistical data. Using this method, complicated data shapes can be categorized. Specifically, a commonly used topological method very used to extract features from a Point Cloud (a set of data points in space) is the Topological Data Analysis (TDA). TDA employs tools from algebraic and combinational topology to draw out properties that express data shapes. It can be considered a key method in attempting to interpret and comprehend characteristics that are otherwise unattainable through the use of other practices due to noise, dimension, or incompletion. It is so unique in its nature that TDA bridges the way between geometry and topology. Successful and remarkable applications have been made in a varied selection of fields, and the range of applications continues to expand. Some of these applications include neuroscience [15], materials science [16], detection and quantification of periodic patterns in data [17; 18], analysis of turbulent flows [19], natural language processing [20], or even detection and classification of breast cancers [21]. However, it has been used in image processing [22], computer vision [23], or signal and time series analysis [24; 25]. Specifically, over the past few years, researchers have also begun to use TDA along with Machine Learning methods [26; 27]. Within TDA, there is an important method called Persistence Homology that can be considered the main tool of TDA. As well as being a modification of the representation of homology using Point Cloud data, this method computes the homological characteristics of datasets. In addition, TDA uses Persistence Diagrams and Persistence Barcodes to represent the abundant homological information about the shape of data. However, note that the use of algorithms of Machine Learning along with Persistence Diagrams or Barcodes is an area of TDA under research, looking for a way to alter these diagrams to be adaptable and congruous with Machine Learning methods. An alternative approach to these two diagrams is Persistence Landscapes. In this work, we hypothesized that using Topological Data Analysis (TDA), some geometric features condensing relevant information about the'shape of data' can be very valuable for the detection and discrimination of VF and VT rhythms, even in noisy and complex signals. Extracted features can then be applied to machine learning classifiers. Thus, the goal of this work is to assess the improvement of the classification performance to detect and discriminate VF and VT episodes, when incorporating a set of TDA-derived geometric features in the feature extraction and selection stage. Note that the main difference with previous works is that these kinds of features have been never applied before in the analysis and classification of ventricular arrhythmias. The main contributions of this work are  
* The proposal of a novel classification procedure using features derived from Topological Data Analysis (TDA). * The application of the proposed classification procedure to the detection and discrimination of VF and VT.