# 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 distinctive feature was the integration of time-frequency analysis with wireless technologies. In [54], a convolutional neural network was used to identify ventricular arrhythmias that require shock. The novelty element was the implementation of this algorithm in an automated context. The method presented in [55] involved the use of variational mode decomposition to extract the characteristics of the ECG signal associated with shockable ventricular fibrillation. The novelty element was the application of this decomposition technique. The paper [56] involved the efficient selection of meaningful features from ECG signals and the use of a C4.5 classifier for distinguishing different types of ventricular arrhythmias. The novelty element consisted of the feature selection method and the application of a specific classifier. The method proposed in [57] combined the adaptive decomposition of the variable mode with a boosted-CART classifier to improve the detection of ventricular arrhythmias. The novelty element was the integration of the two techniques for better performance. In [58], a machine learning-based approach was used to classify ventricular fibrillation and tachycardia. The novelty element was the application of machine learning technologies for the analysis of ECG signals. The method proposed in [60] involved the rapid detection of ventricular arrhythmias using blocks of 3 s ECG signals. The novelty element was the efficiency and speed in the detection of these arrhythmias. The study [60] used the digital Taylor-Fourier transform to analyze ECG signals and detect life-threatening ventricular arrhythmias. The element of novelty consisted of the application of this transformation technique. The paper [61] compared the effectiveness of derived ECG features, either in combination or individually, in predicting defibrillation success in patients with out-of-hospital cardiac arrest. The novelty element was the evaluation of the performance of these features in different configurations. In the study [62], real-time detection of ventricular fibrillation was implemented using an embedded microcontroller in a pervasive environment. The novelty element was the integration of technology in an environment with limited resources. \begin{table}
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**DETECTION** & **Se (\%)** & **Sp (\%)** & **Acc (\%)** & **Win (s)** & **Database** & **Author and Year** \\ \hline Time–frequency representation images as & & & & & & \\ input dataset for Bagging & 98.46 & 98.43 & 98.44 & & AHAMIT-BIH & Mjahad 2017 [79] \\ Classifier (BAGG) & & & & & & \\ \hline Time–frequency representation images as & & & & & \\ input dataset for artificial neural network & 95.56 & 98.8 & 98.19 & 1.2 & AHAMIT-BIH & Mjahad 2017 [79] \\ classifiers (ANNC) & & & & & & \\ \hline Time domain, frequency domain, and & & & & & \\ complexity measure and & & & & & \\ ensemble classifier & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)The method proposed in [63] involved the use of discrete wavelet transform (DWT) and a decision tree classifier for the detection of tachycardia and ventricular fibrillation. The novelty element was the application of DWT in the analysis of ECG signals. In [11], a support vector machine (SVM) enhanced by the \"boosting\" machine learning technique was used for ventricular fibrillation rhythm detection. The novelty element was the optimization of the combination of variables to improve performance. The method proposed in [64] involved the analysis of ECG signals using a Boltzmann network. The novelty element was the application of this neural network technique. The paper [65] investigated the prediction of shock success for potentially lethal cardiac arrhythmias using a common set of parameters. The element of novelty consisted in the identification of relevant common parameters. The method proposed in [66] involved the classification of tachycardia and ventricular fibrillation based on Lempel-Ziv complexity and empirical mode decomposition.