## 1 Introduction  
VF is a serious heart condition characterized by rapid and irregular contractions of the heart's ventricles, which prevent blood from pumping through the body. VF is the most usual source of sudden cardiac death. Most events of VF take place during the first 48 to 72 h after a burst of signs. As a demonstration of ischemia, it is related to the absence of reperfusion in the infarct-associated artery [1]. VF can be classified into primary and nonprimary. Primary VF usually takes place in less than 48 h after myocardial infarction (MI). It is not related to heart failure or recurrent ischemia. Nonprimary VF designate all other episodes. VF is more abundant in subjects with MI complicated by heart failure or recurrent ischemia. VF is fatal if not handled. It is considered that the survival rate decreases by approximately 10% per minute for patients after the onset of VF. Defibrillation is the most efficient therapy for VF. A biphasic waveform defibrillator, with a primer kick of 120 to 200 joules, is often a successful therapeutic device. Sudden cardiac death is defined as an unexpected death within 24 h of the onset of symptoms or due to a sudden cardiovascular event without a known medical history. Sudden death can be caused by a variety of conditions, including VF, which is responsible for approximately 75% of sudden cardiac deaths (SCD) in adults. While certain heart conditions can predict the risk of sudden death, VF can occur without warning in patients with no known heart conditions. Early detection of VF is crucial to prevent sudden death and improve the patient's chances of survival. Patients with VF must receive immediate treatment to restore normal heart rhythm and prevent serious complications. In addition, patients at high risk of developing VF should be monitored regularly to detect its early signs and prevent complications [2; 3; 4]. VF is detected by analyzing electrocardiographic signals (ECG). Traditionally, the ECG is continuously monitored to detect signs of ventricular fibrillation, but this process can be difficult and time-consuming, especially in patients who have rare or asymptomatic cardiac arrhythmias. A more efficient method of detecting ventricular fibrillation is the analysis of the ECG signal by means of signal processing algorithms. These algorithms can detect signs of ventricular fibrillation, even if they are hidden by ECG noise. Signal processing algorithms are currently used in most medical devices for monitoring patients with heart disease [5; 6; 7]. The use of artificial intelligence in the analysis of medical data or in the processing of biomedical signals is a field that has exploded in terms of the literature and a number of practical implementations in recent years. From monitoring bracelets to deep learning neural networks, from software systems to hardware implementations, from simple classification algorithms like KNN to deep learning neural networks, only the researchers' imagination is the limit that can cause problems regarding the applicability of AI in the processing of medical data and their applicability with the aim of increasing life expectancy and patient compliance [8; 9]. In the processing of the ECG signal, the extraction of important features is an essential step for the detection of ventricular fibrillation. These features may include peaks and valleys of the QRS complex, T wave duration, and QT interval, which are associated with certain types of arrhythmias. The correct extraction of these features can help identify ventricular fibrillation and avoid erroneous detection of other types of arrhythmias. In addition, the extraction of important features can be improved using artificial intelligence (AI) [10; 11]. For example, machine learning algorithms can be trained to automatically identify and extract meaningful features from the ECG signal, which can reduce errors and improve the accuracy of ventricular fibrillation detection.