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.

Photodynamic and antibacterial studies of template-assisted Fe2O3-TiO2 nanocomposites.

Fe2O3-TiO2 (FT) nanocomposites were successfully synthesized by template-assisted precipitation reaction using Polyvinylpyrrolidone-Polyethylene glycol (PVP-PEG), Tween-80 (T-80) and Cetyltrimethylammomium bromide (CTAB) as templates. The prepared nanocomposites were characterized by XRD, SEM, EDX, UV-DRS, FT-IR, and FT-Raman spectroscopic analysis. The photohemolysis studies were done in human erythrocytes and the cell viability studies were done in HeLa cell lines under the irradiation of an LED light source. The photodynamic studies were performed under two different experimental conditions, such as varying concentrations as well as a time of irradiation. The nanocomposites exhibit significant photodynamic activity via the generation of reactive oxygen species (ROS) under the light source. The results show that the PVP-PEG-assisted Fe2O3-TiO2 (FT-PVP-PEG) nanocomposite has more potential for photodynamic activity in the presence of an LED light source. Also, the antibacterial effect of the samples was investigated against gram-negative bacteria (Escherichia coli). Among all nanocomposites, FT-PVP-PEG shows remarkable antibacterial activity against E. coli. Moreover, the template-assisted nanocomposites protect the biomolecules from the toxicity generated by the magnetic nanoparticles (NPs). The template-assisted FT nanocomposites for the field of photodynamic activity have been experimentally shown for the first time.

Differentiation of fluctuations in uterine contractions associated with Term pregnancies using adaptive fractal features of electromyography signals.

Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range (Hs) and long range (Hl) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions.

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.

Analysis of uterine electromyography signals in preterm condition using multifractal algorithm.

In this work, an attempt has been made to analyze the preterm (gestation period $\leq 37$ weeks) condition using uterine electromyography (EMG) signals and multifractal detrended fluctuation analysis (MFDFA). The signals recorded from the electrodes placed on the surface of abdomen are used for this study and these are obtained from a publically available online database. These signals are preprocessed using 4-pole digital Butterworth filter. The preprocessed signals are subjected to MFDFA to extract multifractal features namely maximum singularity exponent, peak singularity exponent, strength of multifractality and exponent index. Generalized Hurst exponent extracted from the signals indicate that uterine EMG signals show multifractal behavior in preterm condition. Among the extracted features the coefficient of variation is found to be lower for peak singularity exponent. This indicates that this feature have lower inter-subject variability. Hence, it appears that the multifractal features can help in the assessment of uterine EMG signals for preterm detection.

Spectral, morphological and antibacterial studies of ß-cyclodextrin stabilized silver - Chitosan nanocomposites.

The aim of the study is to investigate the antibacterial properties and characterization of ß-cyclodextrin (ß-CD) stabilized silver - chitosan nanocomposite (Ag-Cts NCs). An effective and eco-friendly technique for the synthesis of Ag-Cts NCs in the presence of a strong stabilizing agent ß-CD is described. The well formed nanocomposites were characterized by the Ultraviolet Visible spectroscopy (UV-Vis), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR), Photoluminescence spectroscopy (PL), Scanning electron microscope (SEM/EDS), Atomic force microscope (AFM), High resolution transmission electron microscope (HR-TEM) and Zeta potential measurement (ZP). The results confirmed that the poly dispersed Ag-Cts NCs are less than 15nm in size with spherical shape and show good stability. The antibacterial activity was also investigated and ß-CD coated Ag-Cts NCs showed a promising bacterial activity against gram negative Escherichia coli (E. coli) and gram positive Staphylococcus aureus (S. aureus) micro-organism.