Multiscale multifractal analysis of 12-lead ECG signals of mice based on wavelet transform and coarse-grained approach

In recent years, the nonlinear analysis of electrocardiogram (ECG) signals using multifractal theory has been proved to be effective. In order to further study the features in normal and pathological ECG signals with multifractal analysis, drugs experiments were conducted on mice. Mice received various drugs to imitate different physiological and pathological conditions, and the distributions of the singularity strength range with different scale factors from the 12-lead ECG signals of healthy and drug injected mice were calculated, using coarse-grained approach and wavelet transform respectively. The results showed that there is a certain range of characteristic frequency in the curve of singularity strength range versus scale factors from the multiscale multifractal analysis results on mice ECG signals. In this range, the singularity strength range Δα gets the maximum value, which can best represent the complex fractal structures and dynamic characteristics of the heart, and be most sensitive to distinguish various physiological and pathological states.