## 1 Introduction
In addition to the detection of ventricular fibrillation, AI can also be used to predict the occurrence of this arrhythmia. For example, automatic learning algorithms can be trained to detect changes in morphological parameters characteristic of the ECG signal or in parameters extracted in the time domain, frequency or parameters characterizing the complexity of the ECG signal [12; 13; 14]. Thus, in recent years, signal processing technology and the use of artificial intelligence in the detection and prediction of VF have significantly improved the ability of doctors to diagnose and treat this cardiac condition that represents a medical emergency. When there is ventricular fibrillation, the electrocardiogram shows an irregular wave pattern with a frequency in the domain (150 cycles... 500 cycles)/minute and varying size. It does not exist as QRS complexes, ST segments, P or T waves. A reduction in adenosine triphosphate (ATP) level, which is the main origin of energy, can cause a great-wave VF to become a reduced-wave VF. This condition is associated with a small survival measure, as it indicates exhaustion of functional reserves and a decreased response to treatment. It is important to distinguish small-wave VF from asystole. To record a VF trace in a conscious patient, it is necessary to check the connections between the monitor and the defibrillator. Various techniques have been proposed for detecting VF by processing electrocardiographic (ECG) signals. In the time domain, these methods include using thresholding methods [15], and the autocorrelation function. The conversion of the ECG signal into a binary one and assessing its complexity is an effective method as well. In the frequency domain, signal band-stop filtering, leakage estimation, and spectrum analysis methods also proved to be efficient. More recent techniques for detecting VF involve wavelet transform, neural networks, and support vector machines (SVMs). Combining characteristics that reflect the frequency and morphological features of the ECG proved to be an efficient method to detect VF [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. In recent years, various methods have been deployed for the identification of VF and VT (in this last condition, the ECG shows a rate of greater than 120 beats per minute and at least three wide QRS complexes in a row). These algorithms are based on current techniques in signal processing and also on pattern recognition or classification by means of artificial intelligence (AI) techniques like deep learning [16; 17; 18; 35]. However, the approaches which need high computational effort may be problematic in achievement on small instruments like fitness brackets or smart watches. Therefore, the usage of ECG traits that require a reduced number of calculations and a classifier with reduced computational requirements but leading to a classification rate close to that of complex algorithms can be a feasible solution. Thus, a successful method in hardware implementation may differ from an optimal method implemented in software. Successful practical implementations represent a compromise between software implementations with maximum success rates and hardware costs. This paper proposes a study on a method of predicting a possible episode of VF 40 or 50 min in advance, as well as the immediate detection of an episode of VF. We considered that the 40 or 50 min interval for prediction is sufficient for the patient to take some measures to help him overcome the episode, measures such as warning the doctor to help him with defibrillation or medication. Also, the immediate detection of a VF episode is equally important because the chance of survival is inversely proportional to the interval between the installation of VF and the measures to help the patient overcome this episode [34; 36]. The following section presents the features extracted from the analyzed ECG segments, the databases used for testing and their segmentation method for both VF detection and prediction, and the classifiers used.