# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
###### Abstract  
Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF. ventricular fibrillation; phase space reconstruction; topological data analysis; persistent homology; electrocardiogram signal Article  
Early ventricular fibrillation Prediction Based on Topological Data Analysis of ECG Signal  
[PERSON]; [PERSON]; [PERSON] Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal. _Appl. Sci._**2022**, 12, 10370. [[https://doi.org/10.3390/app122010370](https://doi.org/10.3390/app122010370)]([https://doi.org/10.3390/app122010370](https://doi.org/10.3390/app122010370))  
Academic Editor: [PERSON]  
Received: 16 July 2022  
Accepted: 11 October 2022  
Published: 14 October 2022  
**Published: 14 October 2022**  
resuscitation. In terms of medium lengths, [PERSON] et al. predicted VF 5 min in advance with an accuracy of 95% using 38 ECG records [8], and [PERSON] et al. predicted VF 4 min 31 s in advance with an accuracy of 98.44% using 64 ECG records [9]. There are adequate databases available for medium length predictions with sufficient warning time, so this study focuses on the forecast time of medium lengths for VF prediction. Moreover, feature extraction plays an essential role in VF prediction accuracy. Heart rate variability (HRV) estimation has been used as the main feature to predict VF [10; 11], including time domain [5; 7], frequency domain [7], time-frequency [5; 12], and nonlinear domain [5; 8; 12]. In addition, morphological features of ECG signals have also been employed to predict VF [13], such as QRS interval and Q-wave peak [4], as well as T-wave, ST-segment, and QT interval [14]. Recently, phase space reconstruction (PSR) has been applied to observe the nonlinear dynamical behavior of ECG series for VF prediction. PSR treats heart activity as a nonlinear dynamical system, which can be mapped from a one-dimensional time series to a multidimensional space using time-delay embedding parameters [15; 16]. The box-counting method is commonly used for extracting statistical features from PSR trajectories [17]. For ECG-based VF prediction, statistical features mainly include mean (\(\mu\)), variance (\(\sigma\)), skewness (\(\gamma\)), coefficient of variation (CV), kurtosis (\(\beta\)), J index, and self-similarity features [9; 18; 19; 20]. It follows that the PSR method is feasible to predict VF. Topological data analysis (TDA) features characterize the internal topological structure of PSR trajectories and are sensitive to the weak changes of ECG signals. Therefore, we propose a TDA-based VF prediction method to improve prediction accuracy.