## 1 Introduction  
The most common causes of sudden death are cardiovascular diseases, which are among the leading causes of death worldwide. One of the cardiovascular diseases with the highest mortality is Ventricular Fibrillation (\(VF\)), which is a cardiac arrhythmia condition produced by a disorganized electrical activity in the ventricles. During \(VF\), the ventricles contract with an absence of an effective beat causing a pumping failure which could lead to a sudden death if the patient is not adequately treated within a few minutes. Defibrillation is the only definitive treatment for \(VF\). It consists of applying a high voltage electric shock on the patient's chest, facilitating the restart of a normal electrical cardiac activity [1; 2; 3]. However, the success of defibrillation is inversely proportional to the interval of time lapsed from the beginning of the episode to the application of the discharge. There are many difficulties in diagnosing \(VF\): On the one hand, the intrinsic characteristics of the \(VF\) signal (lack of organization, irregularity, etc.) and, on the other hand, the great similarity between VF and other cardiac pathologies such as ventricular tachycardia (\(VT\)) [4], especially in early stages of \(VF\). The differentiation between \(VT\) and \(VF\) is quite complex: The wrongful diagnosis of \(VF\) for a patient that really suffers of \(VT\) can cause serious complications at the time of applying the therapy corresponding to \(VF\) (high voltage electrical discharge), as it may cause \(VF\) to the patient. On the contrary, if \(VF\) is incorrectly interpreted as \(VT\) or any other cardiac rhythm, the result can also be dangerous for the patient's life since the treatment would imply receiving less voltage than the appropriate level. Thus, an effective detection method for distinguishing \(VF\) from \(VT\) is critical in clinical research. The electrocardiogram (ECG) is a non-invasive, low-cost examination tool that has been used as the basic method of diagnosing cardiac conduction disorders by studying the heart rate and morphology of different waves that constitute the cardiac cycle. ECG analysis is a good source of information from which different types of heart disease can be detected. Due to the fact that the ECG signal is a non-stationary random signal, the time domain analysis does not prove to be sufficiently sensitive to the distortions of the ECG waveforms. However, these methods do not always show all the information that can be extracted from the ECG signals [5; 6], thus losing information on the frequency domain which shows additional information on the signal. Diagnosis in the frequency domain [7] uses methods such as the Fourier transform. Therefore, the analysis in the frequency domain allows to determine the frequencies of the signal. On the other hand, the temporary-type information of the signal is lost, which is a very limited method and is not useful for the analysis of non-stationary signals. Several studies have used mathematical models that combine temporal and spectral information in the same representation. This technique of Time-Frequency Representation (\(TFR\)) is very important in the treatment of non-stationary signals such as the ECG signal, as it distributes the energy of the signal in a two-dimensional time-frequency space [8; 9]. In addition, multiple factors might alter the acquisition and recording of the ECG signal: The influence of the environment, 50-60 Hz mains interference, variations of the base line of low-frequency interference in the range of 0 Hz to 0.5 Hz [10; 11]. On the other hand, there are disturbances of physiological origin such as those of electromyography (EMG). ECG noise reduction has been one of the main fields of research in the last decades since an adequate noise reduction allows a good pre-processing of the signal, extracting the maximum amount of information possible and eliminating ECG signal contamination from other sources.