# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 5 Conclusions  
This study sets out to find a new feature for early VF prediction with enhanced performance. The proposed TDA features provide a new perspective on cardiac activity based on nonlinear dynamics. Experimental results indicate that the proposed features effectively improve the accuracy in the field of medium length VF prediction. The proposed features have the potential to be used in the diagnosis of other cardiac diseases. However, the amount of data in the databases used for this study is limited, which will be supplemented with more ECG-based VF databases in future works. Moreover, the elimination of differences among databases needs to be investigated. This will offer the possibility to predict VF earlier with guaranteed accuracy.  
Conceptualization, F.J.; methodology, T.L. and Z.Z.; validation, T.L. and Z.Z.; investigation, Y.Z.; data curation, T.L.; writing--original draft preparation, F.J., T.L. and Z.Z.; writing--review and editing, Z.Z. and Y.Z.; funding acquisition, F.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61801104, No. 61902058), in part by the Fundamental Research Funds for the Central Universities under Grant N2019002, and in part by the Northeastern University's 15 th session (2020) Innovation Training Program for College Students (No. 210250). The APC was funded by National Natural Science Foundation of China (No. 61801104).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference Index** & **Forecast Time** & **Database** & \
\begin{tabular}{c} **Split Frame** \\ **Length** \\ \end{tabular} & **ECG Features** &
\begin{tabular}{c} **Prediction** \\ **Performance** \\ \end{tabular} \\ \hline
[4] & 15 min & \begin{tabular}{c} NSRDB 9 \\ VFDB 9 \\ SDDB 23 \\ NSRDB 18 \\ CUDB 27 \\ \end{tabular} & 60 s & \
\begin{tabular}{c} 12 morphological \\ features \\ \end{tabular} &
\begin{tabular}{c} Sensitivity 95\% \\ Specificity 90\% \\ Accuracy 84.28\% \\ Accuracy 98.6\% \\ \end{tabular} \\
[6] & 30 s & \begin{tabular}{c} NSRDB 6 \\ MVTDB 78 \\ SDDB 20 \\ NSRDB 18 \\ \end{tabular} & 5 min & \
\begin{tabular}{c} 12 morphological \\ features \\ \end{tabular} &
\begin{tabular}{c} Sensitivity 95\% \\ Specificity 90\% \\ Accuracy 84.28\% \\ Accuracy 98.6\% \\ \end{tabular} \\
[7] & 10 s & \begin{tabular}{c} MVTDB 78 \\ SDDB 20 \\ NSRDB 18 \\ \end{tabular} & 5 min & \
\begin{tabular}{c} 11 HRV features \\ 60 s \\ \end{tabular} &
\begin{tabular}{c} Accuracy 92.2\% \\ Accuracy 95\% \\ Accuracy 95\% \\ Accuracy 98.44\% \\ \end{tabular} \\
[8] & 4 min 31 s &
\begin{tabular}{c} MVTDB 30 +29 \\ PAFDB 12 \\ NSRDB 18 \\ \end{tabular} & 20 s & 5 HRV features & Accuracy 88.64\% \\
[9] & 4 min 31 s & \begin{tabular}{c} **CDB 15** \\ **SDDB 15** \\ **PTBDB 30** \\ \end{tabular} & **5 s,8 s,10 s,15 s** & \
\begin{tabular}{c} **2 box-counting** \\ **features** \\ **7 HRV features** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **95\%** \\ \end{tabular} \\
[12] & 0 s &
\begin{tabular}{c} MVTDB 30 +29 \\ PAFDB 12 \\ NSRDB 18 \\ \end{tabular} & 20 s & 5 HRV features & Accuracy 88.64\% \\
**Our work** & **5 min** & \
\begin{tabular}{c} **CUDB 15** \\ **SDDB 15** \\ **PTBDB 30** \\ \end{tabular} & **5 s,8 s,10 s,15 s** &
\begin{tabular}{c} **2 box-counting** \\ **features** \\ **9 TDA features** \\ **7 HRV features** \\ \end{tabular} & **Accuracy** \\ \hline \hline \end{tabular}
\end{table}
Table 7: List of studies on the ECG-based VF prediction.