## 1 Introduction
Usually, after the initial processing of the signal, several algorithms are applied to obtain characteristics, features, or parameters which are supposed to offer a difference in value depending on the pathology. Typically, these parameters can be redundant or remove relevant information, being necessary to apply different techniques to select the most adequate. After optimisation, selected parameters are intended to serve as input to a classifier responsible for separating classes (associated to a pathology or type of rhythm, in this case), i.e. identified signal types. In order to improve the performance of individual classifiers, the combination of classifiers (multiclassifiers) can improve the performance in separating classes. It is based on constructing a global classifier built from a set of classifiers that can provide interesting information on the representation of data compared to the results achieved using individual classifiers. There are many examples in the literature that have used the combination of classifiers focused towards the field of bioinformatics and biomedical research, geophysical analysis and remote sensing, among others. Out of the most frequently used multi classifiers, Random Forests [12], Bagging [8], Boosting [13], or Random Subspacesare the most commonly employed multiclassifiers. In the case of Random Subspaces, different subsets of attributes are used to train each individual classifier. The Bagging type variety comes from using different subsets of instances to train each individual classifier. Random Forests is a substantial modification of bagging that uses Random Trees as individual classifiers. The Boosting type iteratively trains the individual classifiers, therefore, it modifies the weights of the instances that will use the next individual classifier. There are other methods such as cascading [14], Stacking [15], and Grading [16]. Other examples using a combination of classifiers for ECG signal analysis can be found in the literature as a multiple classifier system [17], a genetic ensembles of classifiers [18], or a classification approach that uses majority voting optimized by the taguchi method [19]. In some cases, a majority voting [20,21], or a combined stacking technique [22]. Other combinations are also applied, e.g., an application of the decision tree to integrate the results of a set of individual neural classifiers (MLP, TSK, and the SVM) working in parallel [23] or a majority voter determining the P-wave absence over seven beats [24]. This work proposes a new strategy for the detection of \(VF\) whose steps are the initial processing of the signal and obtaining its time-frequency representation (\(TFR\)) with its equivalent image (\(TFR\)). The \(TFR\) or \(TFRI\) (both cases will be analysed) is directly entered into an individual classifier or combined, without calculating parameters or extracting features since time-frequency representation contains both temporal and spectral information from the ECG signal, allowing the classifier to have enough information for the detection of different types of cardiac pathologies in real time. Since the ECG is a temporal signal, it is not common to find works converting the temporal signal into an image and further analyse the image, some works used some geometrical features from the ECG in combination with other features entering the classification stage. Other works also extract features from a time-frequency or discrete wavelet transform, but they do not use it as an image. In order to reach the objectives sought, the present work is structured as follows: Section 2 describes the materials and methods, followed by Section 3 which details the initial processing applied to the ECG signal. Section 4 shows the extraction of information. Section 5 presents the individual and combined classification algorithms.