Analyzing Electrocardiogram Signal Complexity with Weighted Entropy

In the present study, the complexity of ECG signals in normal individuals and those with cardiac disorders is analyzed. This analysis is conducted by utilizing a weighted entropy measure computed from the probability distribution of shortest path lengths in the Recurrence network (RN) domain. The results demonstrate distinct statistical complexity between normal and cardiac disorder ECG signals, with higher average weighted entropy in the latter, signifying increased complexity. Weighted entropy shows significant variability within the same category of ECG signals, attributed to individual variations. Furthermore, ECG signals with additional cardiac abnormalities are analyzed, and our analysis revealed that high complexity is observed in those signals.