# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 3 Recent Reference Works on the Topic of Ventricular Fibrillation Detection and Prediction
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**DETECTION** & **Se (\%)** & **Sp (\%)** & **Acc (\%)** & **Win (s)** & **Database** & **Author and Year** \\ \hline Time-delay method using microcontroller & 94.01 & 84.15 & 86.94 & 8 & MITDB & Kwon 2018 [62] \\ \hline Discrete wavelet transform with & 98 & 99.32 & 99.23 & 5 & CUDB VFDB & Mohanty 2019 [63] \\ \cline{2-6}  & 98.25 & 98.18 & 98.2 & 5 & CUDB VFDB MITDB & Panigrahy 2021 [11] \\ \cline{2-6}  & 96.91 & 96.88 & 96.89 & 2 & CUDB VFDB MITDB & Panigrahy 2021 [11] \\ \hline Boltzmann & 92.5 & & & 64-sample & AHA MIT-BIH & Mjahad 2014 [64] \\ \hline Discriminant analysis & 94.1 & & & 10 & AHA MIT-BIH & Jekova 2007 [65] \\ \hline Filter and counts & 94.4 & 95.9 & 94.7 & 10 & AHA MIT-BIH & Jekova 2004 [52] \\ \hline SVM & 96.2 & 96.2 & 96.3 & 5 & AHA MIT-BIH & Li 2014 [58] \\ \hline Lempel–Ziv and EMD & 98.15 & 96.01 & 97.1 & 1000 & MIT-BIH & Xia 2014 [66] \\ \hline Fuzzy simil app entropy & 97.98 & 97.03 & 97.5 & 1000 & CUDB MIT-BIH & Xie 2011 [67] \\ \hline Approximate entropy & 91.84 & 90.2 & 91 & 1000 & MIT-BIH & Xie 2011 [67] \\ \hline EMD and app entropy & 90.47 & 91.66 & 91.2 & & MIT-BIH & Kaur 2013 [68] \\ \hline KNN & 98.1 & 88 & 93.2 & 7 & MIT-BIH & Ibaida 2010 [69] \\ \hline RBF & 91.53 & 90.91 & 91.3 & 7 & MIT-BIH & Ibaida 2010 [69] \\ \hline Type-2 fuzzy & 90.9 & 84 & 100 & & MIT-BIH & Tan 2007 [70] \\ \hline Neural SOM & 100 & 48 & 88 & & MIT-BIH & Tan 2007 [70] \\ \hline Type-2 TSK fuzzy & 93.3 & 92 & 100 & & MIT-BIH & Phong 2009 [71] \\ \hline Type-2 Mamdani fuzzy & 86.6 & 88 & 100 & & MIT-BIH & Phong 2009 [71] \\ \hline Random forest classifier & 95.04 & 94.78 & 94.79 & 8 & CUDB MIT-BIH & Verma 2016 [15] \\ \hline SVM & 95 & 99 & & 8 & CUDB MIT-BIH & Alonso-Atienza 2014 [12] \\ \hline Binary decision tree & 95.3 & 94.5 & 94.2 & 8 & CUDB MIT-BIH & Hajeb-Mohammadolipour 2018 [72] \\ \hline SVM & 90.4 & 91.6 & 89.3 & 8 & CUDB MIT-BIH & Mohammadalolipour 2018 [72] \\ \hline LS-SVM classifier with RBF kernels & 85.2 & 82.46 & 83.75 & 8 & CUDB MIT-BIH & Tripathy 2018 [60] \\ \hline BAGG\_KNN\_ANNC & 95.58 & 99.31 & 98.6 & 12 & AHA MIT-BIH & Mjahad 2018 [42] \\ \hline Threshold crossing sample count—TCSC & 80.97 & 98.51 & 98.14 & 8 & CUDB MIT-BIH & Arafat 2011 [73] \\ \hline Time-delay (PSR) & 76.73 & 98.49 & 98.03 & 8 & \begin{tabular}{l} AHA CUDB \\ MIT-BIH \\ \end{tabular} & Amann 2007 [74] \\ \hline Hilbert transform (HILB) & 77.47 & 97.88 & 97.44 & 8 & \begin{tabular}{l} AHA CUDB \\ MIT-BIH \\ \end{tabular} & Amann 2005a [75] \\ \hline Reduced feature set and nonlinear SVM & 74.1 & 94.7 & 83.77 & \
\begin{tabular}{l} AHAMIT-BIH \\ \end{tabular} &
\begin{tabular}{l} Alonso-Atienza \\ 2012 [76] \\ \end{tabular} \\ \hline Generalized discriminant analysis (GDA) and SVM & 95.77 & 99.4 & 99.16 & MIT-BIH & Asl 2008 [13] \\ \hline SVM and feature selection & 92.396 & 99.121 & 99.35 & MIT-BIH & Song 2005 [77] \\ \hline No QRS detection performed, no feature extraction or featureselection involved, convolutional neural network (CNN) & 56.44 & 98.19 & 97.88 & 5 & CUDB MIT-BIH & Acharya 2017 [1] \\ \hline Signal processing and machine learning & 99.988 & 98.401 & 99.194 & 5 & CUDB MIT-BIH & Ibtehaz 2019 [78] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._The method used in [51] involved the analysis of ventricular fibrillation detection algorithms in automated external defibrillators using test data from public databases of ECG signals. The novelty element consisted of the comparison between older and new algorithms, highlighting their reliability. The study [52] implemented a method for real-time detection of ventricular fibrillation and tachycardia using continuous analysis of ECG signals. The algorithm used adaptive filtering and spectral analysis techniques. The novelty element was the implementation of a real-time detection solution, with the results reported during one year of monitoring. The method developed in [53] was intended for continuous personal monitoring of heart rhythms by means of wireless devices.