Introduction: Despite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers.Methods: We analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3–1 Hz, 1–2 Hz, and 2–3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channel-filter) were fed to different classifiers using feature selection techniques. Results: The best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2–3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment.Discussion: Our results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms.

Labor is regarded as increased myometrial activity with a regular contractility pattern. At this final stage of pregnancy, myometrial quiescence is lost, accompanied by altered immune homeostasis. It is well known that the interleukin (IL)-10 family of cytokines modulates immunological responses mainly in epithelial cells, including the endometrium. To investigate their inflammatory profile during labor, we performed a longitudinal study in a group of healthy pregnant women (n = 20) with uncomplicated pregnancies in the third trimester of pregnancy and during active labor. Blood was sampled from pregnant women in the third trimester (gestational age 32-38 weeks, mean 36 ± 2 weeks) and during active labor (39-41 weeks of gestation, mean 40 ± 1 weeks). Serum levels of several cytokines were measured using multiplex immunoassays for both stages, indicating that the concentrations of IL-10, IL-20, IL-22, IL-28A, and interferon (IFN)-γ were significantly decreased during active labor in comparison with third-trimester levels (p < 0.05). Our analysis did not find significant correlations between IL-10, IL-20, IL-22, IL-28A, and IFN-γ levels and gestational age. However, our data suggest that the systemic downregulation of several members of the IL-10 family of cytokines plays an important role in the activation of myometrial smooth cells associated with uterine contractions during active labor. Downregulation of this IL-10 family of cytokines seems to coincide with the well-reported functional progesterone withdrawal during labor. Likewise, lower plasma IFN-γ concentrations may indicate a role for IFN-γ in active labor.

Stress has become a common condition and is one of the chief causes of university course disenrollment. Most of the studies and tests on academic stress have been conducted in research labs or controlled environments, but these tests can not be extended to a real academic environment due to their complexity. Academic stress presents different associated symptoms, anxiety being one of the most common. This study focuses on anxiety derived from academic activities. This study aims to validate the following hypothesis: by using a non-contact method based on the use of remote photoplethysmography (rPPG), it is possible to identify academic stress levels with an accuracy greater than or equal to that of previous works which used contact methods. rPPG signals from 56 first-year engineering undergraduate students were recorded during an experimental task. The results show that the rPPG signals combined with students’ demographic data and psychological scales (the State–Trait Anxiety Inventory) improve the accuracy of different classification methods. Moreover, the results demonstrate that the proposed method provides 96% accuracy by using K-nearest neighbors, J48, and random forest classifiers. The performance metrics show better or equal accuracy compared to other contact methods. In general, this study demonstrates that it is possible to implement a low-cost method for identifying academic stress levels in educational environments.

The aim of this study was to explore cardiac autonomic changes assessed by linear and nonlinear indexes of heart rate variability (HRV) and body composition modifications in breast cancer survivors and cancer-free control women. Women who were breast cancer survivors (BCS, n = 27) and without cancer with similar characteristics (Control, n = 31) were recruited for this study. We calculated some relevant linear and nonlinear parameters of 5 min of RR interval time series such as mean RR interval (RR_ave), the corrected Poincaré index (cSD1/SD2), the sample entropy (SampEn), the long-term fractal scaling exponent (α₂) and 2UV from symbolic dynamics. Additionally, we indirectly assessed body composition measures such as body weight, fat mass, visceral fat rating (VFR), normalized VRF (nVFR), muscle mass, metabolic age, and total body water. We found that diverse HRV indexes and only one body composition measure showed statistical differences (p < 0.05) between the BCS and Control groups. RR_ave: 729 (648–802) vs. 795 (713–852) ms; cSD2/SD1: 3.4 (2.7–5.0) vs. 2.9 (2.3–3.5); SampEn: 1.5 (1.3–1.8) vs. 1.7 (1.5–1.8); α₂: 0.6 (0.3–0.6) vs. 0.5 (0.4–0.5); 2UV: 7.1 (4.3–11.5) vs. 10.8 (6.4–15.7) and nVFR 0.12 (0.11–0.13) vs. 0.10 (0.08–0.12) points/kg, respectively. The nVFR was strongly significantly correlated with several indexes of HRV only in the BCS group.Our findings suggest that BCS exhibit lower parasympathetic cardiac activity and changes in HRV patterns compared to Controls. A concomitant increase of visceral fat, among other factors, may contribute to cardiac autonomic disturbances and changes in HRV patterns in BCS.