Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Measurements, Models, Systems and Design

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CONGRATS - Convolutional Networks in GPU-based Reliability Assessment of Transmission Systems

Monte Carlo Simulation (MCS) is a powerful method frequently used for composite power system adequacy assessment. However it requires a considerable amount of time to provide accurate estimates for the reliability indexes. In the last years, mathematical approaches have been developed, for instance variance reduction techniques, with the aim to speed up this process. More recently, the MCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms, resulting in reduction of the simulation time. In this dissertation, a new approach is developed to shrink simulation time by apllying Convolutional Neural Networks (CNN), trained on a GPU

Social determinants and child survival in Nigeria in the era of Sustainable Development Goals: Progress, challenges, and opportunities

Introduction: Like in many low- and middle-income settings, childhood mortality remains a big challenge in Nigeria—being the second largest contributor to under-five mortality globally, after India. Currently, there is little local evidence to guide policymakers in Nigeria to tailor appropriate social interventions to make the Sustainable Development Goal (SDG) targets of child survival (SDG-3), gender equality (SDG-5), and social inclusiveness (SDG-10) achievable by 2030. In addition, lack of methodological rigor and theoretical foundations of child survival research in Nigeria limit their use for proper planning of child health services. Aims: The basis of this thesis is to understand the complex issues relating to child survival and recommend new approaches to guide policymakers on interventions that will improve child survival in Nigeria. The overarching goal of this thesis is to address the methodological and theoretical shortcomings identified in the previous studies conducted in Nigeria. Using robust interdisciplinary analytic techniques, this thesis assessed the following specific objectives. Objective 1: (a) Compare predictive abilities of the most used conventional statistical time-series methods—ARIMA and Holt-Winters exponential smoothing models, with artificial intelligence technique such as group method of data handling (GMDH)-type artificial neural network (ANN), and (b) estimate the age- and sex-specific mortality trends in child-related SDG indicators (i.e., neonatal and under-five mortality rates) over the 1960s-2017 period, and estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. Objective 2: (a) Identify the social determinants of age-specific childhood (0-59 months) mortalities, which are disaggregated into neonatal mortality (0-27 days), post-neonatal mortality (1-11 months) and child mortality (12-59 months), and (b) estimate the within- and between-community variations of mortality among under-five children in Nigeria. Objective 3: Identify the critical pathways through which social factors (at maternal, household, community levels) determine neonatal, infant, and under-five mortalities in Nigeria. Objective 4: (a) Determine patterns and determinants of geographical clustering of neonatal mortality at the state and regional levels in Nigeria, (b) assess gender inequity for neonatal mortality between urban and rural communities across the regions in Nigeria, and (c) measure gaps in SDG-3 target for neonatal mortality at the state and regional levels in Nigeria. Methods: This thesis is a quantitative study which used two secondary datasets—aggregated historical childhood mortality data from 1960s to 2017 (objective 1), and the latest (2016/2017) Nigeria Multiple Indicator Cluster Survey (MICS) for 36 states and Federal Capital Territory (FCT) in Nigeria (objectives 2-4). To minimize recall bias, analysis was limited to a weighted nationally representative sample of 30,960 live births delivered within five years before the survey. The selection of relevant social determinants of child survival was primarily informed by Mosley-Chen framework. The candidate variables were layered across child, maternal, household, and community-levels. The analytic approaches include artificial intelligence technique (i.e., group method of data handling (GMDH)-type artificial neural network, and multilayer perceptron (MLP) neural network), autoregressive integrated moving average (ARIMA), Holt-Winters exponential smoothing models, spatial cluster analysis, hierarchical path analysis with time-to-event outcome, and multilevel multinomial regression. Results: Progress towards achieving SDG targets – Nigeria is not likely to achieve SDG targets for child survival and, within, gender equity by 2030 at the current annual reduction rates (ARR) under-five mortality rate (U5MR): 1.2%, and neonatal mortality rate (NMR): 2.0%. If the current trend continues, U5MR will begin to increase by 2028. Also, at the end of SDG-era, female deaths will be higher than male deaths (80.9 vs. 62.6 deaths per 1000 live births). To make child-related SDG targets achievable by 2030, Nigeria needs to reduce annual U5MR by 9 times and annual NMR by 4 times the current rate of decrease. Social determinants of childhood mortality – At each stage of early childhood development, there are different factors relating to survival outcomes. Surprisingly, attendance of skilled health providers during delivery was associated with an increased neonatal mortality risk, although its effect disappeared during post-neonatal and toddler/pre-school stages. The observed association requires cautious interpretation because of unavailability of variables on quality of care in MICS dataset to assess how skilled birth delivery impacts child survival in Nigeria. However, there is a possibility of under-reporting under-five mortalities at the community level. Also, it could indicate a functioning referral system that sends the high-risk deliveries to health facilities to a greater extent. There is a large variation (39%) of under-five mortalities across the Nigerian communities, which is accounted for by maternal-level factors (i.e., maternal education, contraceptive use, maternal wealth, parity, death of previous children and quality of perinatal care). Pathways to childhood mortality – Region and area of residence (urban/rural), infrastructural development, maternal education, contraceptive use, marital status, and maternal age at birth were found to operate indirectly on neonatal, infant and under-five survival. Female children, singleton, children whose mothers delivered at least two years apart and aged 20-34 years survived much longer. Specifically, women from Northern areas of Nigeria were less likely to reside in urban cities and towns than those in the Southern areas. This, in turn, limited their access to social infrastructure and acted as a barrier to maternal education. Without adequate education, women were less likely to use contraceptive methods. Women with no history of contraceptive use were more likely to have childbirths closer together (less than two-year gap), which in turn, negatively impacted child survival. Regional inequities in childhood mortality – There was significant state-level clustering of NMR in Nigeria. The states with higher neonatal mortality rates were majorly clustered in the North-West and North-Central regions, and states with lower neonatal mortality rates were clustered in the South-South and South-East regions. Gender inequity was worse in the rural areas of Northern Nigeria, while it was worse in the urban areas of Southern Nigeria. NMR was disproportionately higher among females in urban areas (except North-West and South-West regions). Conversely, male neonates had higher mortality risks in the rural areas for all the regions. Conclusions: This thesis provides more refined age- and sex-specific mortality estimates for Nigeria. At the current rates, Nigeria will not meet SDG targets for child survival. In addition, this thesis identifies the critical intervention pathways to child survival in Nigeria during the SDG-era. The new estimates may be used to improve the design and accelerate the implementation of child health programmes to attain the SDG targets. Also, it is important for stakeholders to implement more impactful policies that promote maternal education and improve living conditions of women (especially in the rural areas). To address gender inequities, gender-sensitive policies, and community mobilization against gender-based discrimination towards girl-child should be implemented. Further research is required to assess the quality of skilled birth attendants in Nigeria

An Introduction to Randomizing Deep Polynomial Neural Networks

Τα Βαθιά Πολυωνυμικά Νευρωνικά Δίκτυα (ΒΠΝΔ) αντιπροσωπεύουν μια νέα οικογένεια συναρτήσεων που έχουν επιδείξει πολλά υποσχόμενα αποτελέσματα σε μια ποικιλία απαιτητικών εργασιών, όπως η παραγωγή εικόνων, η επαλήθευση προσώπου και η εκμάθηση αναπαράστασης 3D mesh. Παρά την πρόσφατη εισαγωγή τους στον τομέα της Βαθιάς Μηχανικής Μάθησης, εξακολουθούν να υπάρχουν πολλές πτυχές των ΒΠΝΔ που χρήζουν περαιτέρω εξερεύνησης προκειμένου να εδραιώσουν τη θέση τους μεταξύ άλλων αρχιτεκτονικών Βαθιάς Μάθησης. Μια τέτοια πτυχή είναι η διερεύνηση των τεχνικών κανονικοποίησης. Η κανονικοποίηση παίζει κρίσιμο ρόλο στη βελτίωση της ικανότητας γενίκευσης ενός μοντέλου, στη μείωση της υπερπροσαρμογής και στη βοήθεια όσον αφορά στη μείωση του πλεονασμού του δικτύου μέσω της εισαγωγής στοχαστικότητας. Ωστόσο, η εφαρμογή τεχνικών κανονικοποίησης στα ΒΠΝΔ παραμένει σε μεγάλο βαθμό ανεξερεύνητη. Με έντονο κίνητρο από το συγκεκριμένο κενό στην τρέχουσα βιβλιογραφία, αυτή η διατριβή στοχεύει να προσφέρει μια εισαγωγική διερεύνηση του τρόπου με τον οποίο η κανονικοποίηση μπορεί να εφαρμοστεί στα ΒΠΝΔ. Προτείνονται και αξιολογούνται δύο μέθοδοι κανονικοποίησης, η χειροκίνητη μείωση βαθμού και η εφαρμογή του dropout. Η χειροκίνητη μείωση βαθμού περιλαμβάνει τη μείωση του βαθμού των τανυστών παραμέτρων στα ΒΠΝΔ, ενώ η κανονικοποίηση μέσω dropout περιλαμβάνει την τυχαία αδρανοποίηση στοιχείων των τανυστών παραμέτρων κατά τη διάρκεια της εκπαίδευσης του δικτύου. Η πειραματική αξιολόγηση στα σύνολα δεδομένων MNIST και CIFAR10 κάτω από διαφορετικά επίπεδα προσθήκης θορύβου και κακόβουλων επιθέσεων αποκαλύπτει ότι τα ΒΠΝΔ είναι εντυπωσιακά ανθεκτικά σε εκείνα τα είδη επιθέσεων. Επιπλέον, τα μοντέλα αποδίδουν καλύτερα και υπερπροσαρμόζονται λιγότερο όταν κανονικοποιούνται με χειροκίνητη μείωση βαθμού, ενώ η κανονικοποίηση μέσω dropout συνήθως οδηγεί σε υποβάθμιση της απόδοσης. Αυτή η προσπάθεια σκοπεύει να χρησιμεύσει ως ένα ολοκληρωμένο θεμέλιο, προσφέροντας πολύτιμες γνώσεις και ευρήματα που θα συμβάλλουν σημαντικά και θα παρακινήσουν τη μελλοντική έρευνα σε αυτόν τον τομέα.Deep Polynomial Neural Networks (DPNNs) represent a novel family of function approximators that have demonstrated promising results in a variety of challenging tasks, including image generation, face verification, and 3D mesh representation learning. Despite their recent introduction to the field of Deep Learning, there are still many aspects of DPNNs that warrant further exploration in order to solidify their standing among other Deep Learning architectures. One such aspect is the investigation of regularization techniques. Regularization plays a crucial role in improving the generalization ability of a model, reducing overfitting, and aiding in the reduction of network redundancy through the introduction of stochasticity. However, the application of regularization techniques to DPNNs remains largely unexplored. Strongly motivated by this gap in the current understanding, this thesis aims to provide an introductory exploration into how regularization can be applied to DPNNs. Two regularization methods, explicit rank reduction and dropout regularization, are proposed and evaluated. Explicit rank reduction involves reducing the rank of parameter tensors in DPNNs, while dropout regularization involves randomly omitting elements of parameter tensors during training. Experimental evaluation on the MNIST and CIFAR10 datasets under varying levels of noise addition and adversarial attacks reveals that DPNNs are impressively resilient to adversarial perturbations. Furthermore, they perform better and overfit less when regularized with explicit rank reduction, while dropout regularization typically leads to a degradation in performance. This endeavor serves as a comprehensive foundation, offering valuable insights and findings that will significantly contribute to and stimulate future research in this domain

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI

Temporal - spatial recognizer for multi-label data

Pattern recognition is an important artificial intelligence task with practical applications in many fields such as medical and species distribution. Such application involves overlapping data points which are demonstrated in the multi- label dataset. Hence, there is a need for a recognition algorithm that can separate the overlapping data points in order to recognize the correct pattern. Existing recognition methods suffer from sensitivity to noise and overlapping points as they could not recognize a pattern when there is a shift in the position of the data points. Furthermore, the methods do not implicate temporal information in the process of recognition, which leads to low quality of data clustering. In this study, an improved pattern recognition method based on Hierarchical Temporal Memory (HTM) is proposed to solve the overlapping in data points of multi- label dataset. The imHTM (Improved HTM) method includes improvement in two of its components; feature extraction and data clustering. The first improvement is realized as TS-Layer Neocognitron algorithm which solves the shift in position problem in feature extraction phase. On the other hand, the data clustering step, has two improvements, TFCM and cFCM (TFCM with limit- Chebyshev distance metric) that allows the overlapped data points which occur in patterns to be separated correctly into the relevant clusters by temporal clustering. Experiments on five datasets were conducted to compare the proposed method (imHTM) against statistical, template and structural pattern recognition methods. The results showed that the percentage of success in recognition accuracy is 99% as compared with the template matching method (Featured-Based Approach, Area-Based Approach), statistical method (Principal Component Analysis, Linear Discriminant Analysis, Support Vector Machines and Neural Network) and structural method (original HTM). The findings indicate that the improved HTM can give an optimum pattern recognition accuracy, especially the ones in multi- label dataset