Multifractal analysis of term and preterm uterine emg signals using wavelet leaders

The purpose of this work is to analyze the multifractal features of uterine Electromyography (EMG) signals for the progression of pregnancy in term condition and to differentiate term (period $> 37$ weeks of gestation) and preterm (period $\leq 37$ weeks of gestation) conditions using Wavelet Leaders (WL) algorithm. For this study, the signals recorded from the surface of abdomen during the second (T1 and P1) and third trimester (T2) are considered from an online database. The signals are preprocessed and multifractal analysis is applied to compute the multifractal spectrum. Three features such as minimum $(\alpha{\min})$, maximum $(\alpha{\max})$ and peak $(\alpha{0})$ singularity exponents are extracted from the multifractal spectrum for analyzing the signals in T1, T2 and P1 groups. It is observed that there is a shift in the spectrum with increase in the order of wavelet. $\alpha{\min}$ and $\alpha{\max}$ are able to differentiate signals in T1-P1 and T1- T2 groups respectively. $\alpha{0}$ is found to be consistent and has statistical significance in discriminating signals in all the considered groups. Hence, it appears that these multifractal features can help in investigating the progressive changes in uterine muscle contractions during pregnancy and differentiates term and preterm conditions at an early stage.