# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 1 Introduction  
The electrocardiogram (ECG) is one of the most widespread non-invasive methods of monitoring the electrical activity of the heart. It is essential in the diagnosis and management of a wide range of cardiac conditions. Ventricular fibrillation (VF) is a severe and potentially fatal arrhythmia that requires immediate medical intervention, usually in the form of defibrillation. VF is characterized by chaotic and rapid electrical activity of the ventricles, resulting in the inability of the heart to pump blood efficiently. If not treated immediately, VF can lead to sudden cardiac death. VF is caused by multiple foci of excitation in the ventricles, which generate unsynchronized electrical impulses. These foci disrupt the coordinated contraction of the ventricles, leading to inefficient mechanical activity. According to statistics, sudden cardiac death accounts for approximately 50% of all cardiovascular deaths and 15-20% of all natural deaths in the United States [1]. Each year in the United States, an estimated 325,000 people suffer an out-of-hospital cardiac arrest [2]. Of these, ventricular fibrillation is one of the major causes, being involved in approximately 75% of sudden cardiac arrest cases at the onset of the event [3]. The human heart is an electrically active organ that functions based on electrical impulses generated by the sinoatrial (SA) node, also known as the heart's natural pacemaker. These impulses propagate through the tissues of the heart, causing the heart muscle tocontract and relax, which results in blood being pumped throughout the body. The ECG signal is recorded by placing electrodes on the surface of the skin, which detects the electrical potential differences generated by the electrical activity of the heart. Early and accurate detection of VF is essential to increase the patient's chances of survival. Studies show that for every minute of delay in defibrillation, the chances of survival decrease by about 10% [4]. In this context, the development and implementation of advanced ECG signal processing methods for rapid and accurate detection of VF are of critical importance. Recent technological advances in the field of signal processing have allowed the development of sophisticated algorithms for the detection and analysis of cardiac arrhythmias. These advances include the use of machine learning, deep learning neural networks, and advanced mathematical transformations to improve the accuracy and speed of VF detection. A 2020 study demonstrated that using convolutional neural networks (CNNs) to detect ventricular fibrillation from ECG signals achieved over 95% accuracy [5]. Also, the use of discrete wavelet transform (DWT) to preprocess ECG signals before applying machine learning algorithms led to significant improvements in arrhythmia detection accuracy, with sensitivities and specificities above 90% [1]. Machine learning has become a powerful tool in ECG data analysis. Machine learning and deep learning algorithms can be trained to recognize complex patterns in ECG signals that are associated with arrhythmias, including ventricular fibrillation. Convolutional neural networks (CNNs) have demonstrated outstanding performance in the classification and detection of ECG signals. CNNs are able to extract relevant features from ECG signals by means of convolutional layers, thereby reducing the need for manual data preprocessing. In [1], the authors used a CNN for arrhythmia detection applied to tachycardia ECG segments. The model achieved over 95% accuracy, demonstrating the potential of these networks in automatic VF detection. In [2], a custom CNN was developed for real-time detection of arrhythmias, achieving similar accuracy. Deep learning, especially recurrent neural networks (RNNs), has also been explored for analyzing ECG signals. RNNs are effective in modeling temporal sequences and can capture the temporal dynamics of ECG signals.