Cyclic spectral analysis of electrocardiogram signals based on GARCH model

Cyclostationary analysis of uterine EMG measurements for the prediction of preterm birth

Preterm birth (gestational age < 37 weeks) is a public health concern that causes fetal and maternal mortality and morbidity. When this condition is detected early, suitable treatment can be prescribed to delay labour. Uterine electromyography (uEMG) has gained a lot of attention for detecting preterm births in advance. However, analyzing uEMG is challenging due to the complexities associated with inter and intra-subject variations. This work aims to investigate the applicability of cyclostationary characteristics in uEMG signals for predicting premature delivery. The signals under term and preterm situations are considered from two online datasets. Preprocessing is carried out using a Butterworth bandpass filter, and spectral correlation density function is adapted using fast Fourier transform-based accumulation method (FAM) to compute the cyclostationary variations. The cyclic frequency spectral density (CFSD) and degree of cyclostationarity (DCS) are quantified to assess the existence of cyclostationarity. Features namely, maximum cyclic frequency, bandwidth, mean cyclic frequency (MNCF), and median cyclic frequency (MDCF) are extracted from the cyclostationary spectrum and analyzed statistically. uEMG signals exhibit cyclostationarity property, and these variations are found to distinguish preterm from term conditions. All the four extracted features are noted to decrease from term to preterm conditions. The results indicate that the cyclostationary nature of the signals can provide better characterization of uterine muscle contractions and could be helpful in detecting preterm birth. The proposed method appears to aid in detecting preterm birth, as analysis of uterine contractions under preterm conditions is imperative for timely medical intervention.