# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 3 Recent Reference Works on the Topic of Ventricular Fibrillation Detection and Prediction
The features used included parameters extracted from time-frequency analyses of ECG signals. The novelty lies in the application of this approach to SCD forecasting. The study [93] aimed to stratify the risk of arrhythmic sudden cardiac death in heart failure patients using machine learning techniques. Features extracted from ECG signals and relevant risk factors for heart failure were used. The novelty element consists of the application of machine learning technologies to identify patients at high risk of SCD. In [94], a machine learning-based approach was proposed for the prediction of sudden cardiac arrest using optimal features of heart rate variability. The method involved extracting relevant features from heart rate variability signals and training a machine learning model. In [95], an integrated index for the detection of sudden cardiac death (SCD) was proposed by using discrete wavelet transform and nonlinear features. The method involved extracting features from ECG signals using the wavelet transform and analyzing the relevant nonlinear features. The novelty lies in the integration of these two techniques to develop a robust index for SCD detection. In [96], an automated method was proposed to predict the risk of sudden cardiac death using Kolmogorov complexity and quantization recurrence analysis extracted from heart rate variability signals. Features extracted from HRV signals were used to train an automatic prediction model. The novelty lies in the use of these two traits to predict the risk of SCD. In [97], it was aimed to improve the accuracy of early detection of sudden cardiac death (SCD) by using a decision forest and support vector machines (SVMs). The method included extracting relevant features from ECG signals and training decision models and SVMs. In [98], a prediction of sudden cardiac death (SCD) based on nonlinear features of heart rate variability and a specific SCD index was proposed. The method included extracting relevant features from HRV signals and calculating a specific index for SCD. In [84], the methods proposed in other papers were compared with the method proposed for the prediction of sudden cardiac death (SCD). The features, analysis methods, and predictive performance of each method were analyzed. The novelty lies in the direct evaluation and comparison of different methods for SCD prediction. The results showed a comparable performance of the proposed method with other approaches in the literature. In [18], two databases from MIT-BIH, the NSR and SCDH database, were used for FV prediction. For a thorough analysis, testing was performed on both ECG segments from both databases and only for the SCDH database. It is found that in the case of using the recordings from both databases, very good results are obtained for the prediction of VF events, even for 50 min in advance, both for the use of a set of classifiers and for an MLP-type neural network. The predictive power using the extracted features is also confirmed by the analysis made only using signals from the SCDH database. This additional testing was performed on ECG segments cut 50 min before VF and 50 min after VF, the latter being considered normal segments that would not predict VF. Thus, when only SCDH databases were used, a rate of 90.3% was obtained with an ensemble of classifiers and 87.3% with MLP.