Identification of Cardiac Autonomic Neuropathy Progression from ECG Signals Using Multiscaled Crucial Events and Multifractal Analysis

Cardiac autonomic neuropathy (CAN) is a condition characterized by the dysfunction of autonomic neurons affecting heart rate regulation, which is often reflected in changes in ECG signals.Identifying the progression of CAN based on ECG changes requires advanced signal complexity analysis due to the subtle variations in signal features across different disease stages.This study investigates the relationship between CAN progression and complexity measures using a novel multiscale modified diffusion entropy analysis (MSMDEA) and multifractal detrended fluctuation analysis (MFDFA).ECG signals of 15minute duration from participants of the Charles Sturt University Diabetes Complications Screening Group in Australia were classified into normal (N)(n=40), early (E)(n=42), and severe (S)(n=10) CAN stages.MSMDEA and MFDFA were applied to quantify the scaling index () of crucial events identified by MDEA using the method of stripes and the fractal spectral density exponents (), respectively.Significant differences in disease progression were observed by comparing MSMDEA values across 20 temporal scaling factors between each pair of groups using post hoc analysis (E/S, N/E, and S/N at p < 0.01).The full ranges of spectra computed from MFDFA distinguished the three pairs of ECG signals at a post hoc test statistical significance of p<0.01.