The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition.

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus ‘best practices’ of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries. This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.

Background: Electrical activity recorded with electroencephalography (EEG) enables the development of predictive models for emotion recognition. These models can be built using two approaches: subject-dependent and subject-independent. Although subject-independent models offer greater practical utility compared to subject-dependent models, they face challenges due to the significant variability of EEG signals between individuals. Objective: One potential solution to enhance subject-independent approaches is to identify EEG channels that are consistently relevant across different individuals for predicting emotion. With the growing use of deep learning in emotion recognition, incorporating attention mechanisms can help uncover these shared predictive patterns.Methods: This study explores this method by applying attention mechanism layers to identify EEG channels that are relevant for predicting emotions in three independent datasets (SEED, SEED-IV, and SEED-V). Results: The model achieved average accuracies of 79.3% (CI: 76.0-82.5%), 69.5% (95% CI: 64.2-74.8%) and 60.7% (95% CI: 52.3-69.2%) on these datasets, revealing that EEG channels located along the head circumference, including Fp₁, Fp₂, F₇, F₈, T₇, T₈, P₇, P₈, O₁, and O₂, are the most crucial for emotion prediction. Conclusion: These results emphasize the importance of capturing relevant electrical activity from these EEG channels, thereby facilitating the prediction of emotions evoked by audiovisual stimuli in subject-independent approaches.

One dimensional Doppler Ultrasound (DUS) is a low cost method for fetal auscultation. However, accuracy of any metrics derived from the DUS signals depends on their quality, which relies heavily on operator skills. In low resource settings, where skill levels are sparse, it is important for the device to provide real time signal quality feedback to allow the re-recording of data. Retrospectively, signal quality assessment can help remove low quality recordings when processing large amounts of data. To this end, we proposed a novel template-based method, to assess DUS signal quality. Data used in this study were collected from 17 pregnant women using a low-cost transducer connected to a smart phone. Recordings were split into 1990 segments of 3.75 s duration, and hand labeled for quality by three independent annotators. The proposed template-based method uses Empirical Mode Decomposition (EMD) to allow detection of the fetal heart beats and segmentation into short, time-aligned temporal windows. Templates were derived for each 15 s window of the recordings. The DUS signal quality index (SQI) was calculated by correlating the segments in each window with the corresponding running template using four different pre-processing steps: (i) no additional preprocessing, (ii) linear resampling of each beat, (iii) dynamic time warping (DTW) of each beat and (iv) weighted DTW of each beat. The template-based SQIs were combined with additional features based on sample entropy and power spectral density. To assess the performance of the method, the dataset was split into training and test subsets. The training set was used to obtain the best combination of features for predicting the DUS quality using cross validation, and the test set was used to estimate the classification accuracy using bootstrap resampling. A median out of sample classification accuracy on the test set of 85.8% was found using three features; template-based SQI, sample entropy and the relative power in the 160 to 660 Hz range. The results suggest that the new automated method can reliably assess the DUS quality, thereby helping users to consistently record DUS signals with acceptable quality for fetal monitoring.

Low birth weight (LBW), known as the condition of a newborn weighing less than 2500 g, is a growing concern in the United States (US). Previous studies have identified several contributing factors, but many have analyzed these variables in isolation, limiting their ability to capture the combined influence of multiple factors. Moreover, past research has predominantly focused on maternal health, demographics, and socioeconomic conditions, often neglecting paternal factors such as age, educational level, and ethnicity. Additionally, most studies have utilized localized datasets, which may not reflect the diversity of the US population. To address these gaps, this study leverages machine learning to analyze the 2022 Centers for Disease Control and Prevention’s National Natality Dataset, identifying the most significant factors contributing to LBW across the US. We combined anthropometric, socioeconomic, maternal, and paternal factors to train logistic regression, random forest, XGBoost, conditional inference tree, and attention mechanism models to predict LBW and normal birth weight (NBW) outcomes. These models were interpreted using odds ratio analysis, feature importance, partial dependence plots (PDP), and Shapley Additive Explanations (SHAP) to identify the factors most strongly associated with LBW. Across all five models, the most consistently associated factors with birth weight were maternal height, pre-pregnancy weight, weight gain during pregnancy, and parental ethnicity. Other pregnancy-related factors, such as prenatal visits and avoiding smoking, also significantly influenced birth weight. The relevance of maternal anthropometric factors, pregnancy weight gain, and parental ethnicity can help explain the current differences in LBW and NBW rates among various ethnic groups in the US. Ethnicities with shorter average statures, such as Asians and Hispanics, are more likely to have newborns below the World Health Organization’s 2500-gram threshold. Additionally, ethnic groups with historical challenges in accessing nutrition and perinatal care face a higher risk of delivering LBW infants.