Analysis of uterine electromyography signals using time-frequency based topological features for detection of preterm birth

Detection of preterm birth (gestational week < 37) is a global priority as it causes major health problems to neonates. Assessment of uterine contractions (burst) is required to detect and prevent the threat of preterm birth. Uterine electromyography (uEMG) is widely preferred to measure the uterine contractions noninvasively. These signals are nonstationary in nature. It can be handled by topological data analysis (TDA) effectively. Therefore, TDA can be used to explore the characteristics of uEMG burst signals. In this study, an attempt has been made to distinguish term (gestational week ≥ 37) and preterm conditions using timefrequency based topological features in uEMG burst signals. These signals are obtained from the publicly available online dataset. The annotated burst signals are segmented and subjected to a short time Fourier transform. The transformed real and imaginary Fourier coefficients are plotted in the complex plane and the envelope of the data points are computed using the alpha-shape technique. Four topological features such as, area, perimeter, circularity and ellipse variance are extracted. These features are statistically analyzed. The coefficient of variation (CoV) is calculated to measure the inter-subject variations. The results show that the proposed method is able to discriminate between term and preterm conditions. The extracted features namely, area and perimeter exhibit significant difference (p < 0.05) between these two conditions. The CoV of the perimeter is observed to be low, implying that this feature can handle inter-subject variations in burst signals. The extracted topological features are useful to analyze the characteristics of term and preterm pregnancies

Analysis of Frequency Bands of Uterine Electromyography Signals for the Detection of Preterm Birth

In this work, an attempt has been made to analyze the influence of the frequencies bands in uterine electromyography (uEMG) signals on the detection of preterm birth. The signals recorded from the women’s abdomen during pregnancy are considered in this study. The signals are subjected to preprocessing using digital bandpass Butterworth filter and decomposed into different frequency bands namely, 0.3-1.0 Hz (F1), 1.0-2.0 Hz (F2) and 2.0-3.0Hz (F3). Spectral features namely, peak magnitude, peak frequency, mean frequency and median frequency are extracted from the power spectrum. Classification models namely, k-nearest neighbor, support vector machine and random forest are employed to distinguish the term and preterm conditions. The results show that the features extracted from these frequency bands are able to differentiate term and preterm condition. Particularly, the frequency band F3 performs better than other frequency bands. The features associated with these frequencies along with random forest classification model achieves a maximum accuracy of 75.2%. Thus, these measures could be used to accurately detect the preterm birth well in advance.

Analysis of Fluctuations of Uterine Contractions in Preterm Pregnancies using Adaptive Fractal Features of Electromyography Signals

Analysis of fluctuations of uterine contractions under varied gestational ages is clinically significant. In this work, fluctuations associated with Preterm pregnancies are analyzed. For this, uterine electromyography (uEMG) signals in Preterm condition with varied gestational ages are considered. The signals are subjected to Adaptive Fractal Analysis (AFA) where a global trend is identified using overlapping windows of varying orders. The signal is detrended and fluctuation function is estimated. Hurst exponent features are extracted and analyzed statistically. Results show that AFA is able to characterize the variations in the fluctuations of Preterm uEMG signals. The feature values are distinct and vary with gestational age. Coefficient of variation is observed to be low, indicating that these features could handle inter-subject variability of Preterm signals. As early diagnosis of premature delivery is imperative for timely medical intervention and treatment, it appears that the proposed features could aid in determining the changes in uterine muscle contractions in Preterm condition.