Wavelet permutation entropy analysis of Ventricular Fibrillation and Sudden Cardiac Death ECG signals

In this paper, we applied wavelet permutation entropy to analyze the Ventricular Fibrillation (VF) signals and Sudden Cardiac Death (SCD) signals for making an effective distinction from normal sinus rhythm (NSR) signals. Firstly, three different ECG signals are decomposed by wavelet and reconstructed in each single layer. Then highly discriminated frequency band will be chosen as our target band. Furthermore, under the circumstances of different series length, embedding dimension and delay time, the main work is to distinguish the three ECG signals in different frequency bands based on the permutation entropy (PE). The results show that permutation entropy method can make a distinction between normal and abnormal ECG signals which aren't decomposed, but the effect of decomposing with wavelets is better more. And the highest discriminated frequency band is from 15.625 Hz to 31.25 Hz. From the point of different data length, embedding dimension and delay time, it was found that permutation entropy method have different effects and the findings may assist cardiac clinical diagnosis.