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

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal. to map MECG to FECG efficiently. The high correlation between maternal and fetal ECG parts decreases the performance of convolution layers. Therefore, the masking region of interest using the attention mechanism is performed for improving signal generators' precision. The sine activation function is also used since it could retain more details when converting two signal domains. Three available datasets from the Physionet, including A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, are used to evaluate the performance. The proposed method could map abdominal MECG to scalp FECG with an average 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on, A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. These results are comparable to the state-of-the-art; thus, the proposed algorithm has the potential of being used for high-performance signal-to-signal conversion

Vascular Endothelial Growth Factor in Serum and Saliva of Oral Lichen Planus and Oral Squamous Cell Carcinoma Patients

Background: Oral lichen planus (OLP) is considered as a potentially malignant disorder and vascular endothelial growth factor (VEGF) may play a key role in cancer development. The aim of this study was to compare serum and saliva VEGF among patients with OLP, oral squamous cell carcinoma (OSCC) and a healthy control group. Methods: A cross sectional study was performed on 27 patients with OLP, 27 patients with OSCC and 27 healthy volunteers. The serum and saliva VEGF were assayed by ELISA method. Statistical analysis of ANOVA was used. Results: The mean saliva flow rate and serum VEGF in OLP and OSCC patients were significantly lower compared to healthy control group (p<0.05), but there was no significant difference between OLP and OSCC patients. There was no significant difference in mean salivary VEGF among groups. Conclusion: It seems that saliva VEGF may not be a good biomarker for OLP and OSCC

Comparative Evaluation of EGF in Oral Lichen Planus and Oral Squamous Cell Carcinoma

Oral lichen planus (OLP) is classified as a potential malignant disorder, and epidermal growth factor (EGF) may play a key role in cancer development. The aim of this study was to compare serum and saliva EGF among patients with OLP and oral squamous cell carcinoma (OSCC). A cross-sectional study was performed on 27 patients with OLP (10 reticular and 17 atrophic-erosive forms), 27 patients with OSCC and 27 healthy control group. The study was conducted at the Cancer Department, Clinic of Oral Medicine, Tehran University of Medical Sciences. The serum and saliva EGF were assayed by ELISA method. Statistical analysis of ANOVA was used. The mean serum EGF in OLP and OSCC patients was significantly lower compared to healthy control group (P < 0.05), but no significant difference was observed between OLP and OSCC patients. There was no significant difference in mean salivary EGF among groups. As serum EGF levels appear to be statistically similar in OLP and OSCC, it seems that EGF might play a role in the pathogenesis of OLP and its cancerization

Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region

Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine learning approach. Therefore, stability feature selection and stacked ensembled decision trees were employed in this classification problem. A total of 573 university students responded to a battery of questionnaires. Three-fold cross-validation with a variety of performance indices was sued. The proposed diagnostic system had excellent balanced diagnosis accuracy (AUC = 0.90 [CI 95%: 0.86&ndash;0.93]) with a high correlation between predicted and observed class labels, fair discriminant power, and excellent class labeling agreement rate. Results showed that 23 items out of all items could accurately diagnose suicide ideation/behavior. These items were psychological problems and how to experience trauma, from the demographic variables, nine items from Post-Traumatic Stress Disorder Checklist (PCL-5), two items from Post Traumatic Growth (PTG), two items from the Patient Health Questionnaire (PHQ), six items from the Positive Mental Health (PMH) questionnaire, and one item related to social support. Such features could be used as a screening tool to identify young adults who are at risk of suicide ideation/behavior

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk

Prosthesis control using undersampled surface electromyographic signals

Amputations can result in disability, permanent physical injury, and even posttraumatic stress disorder. Upper extremity amputations are mostly work-related, and such injuries include about 7% of the total burden of disease. High-functional artificial limbs are not available to most amputees because of their high price and the lack of public health coverage. Thus, there has been a significant interest in the design and fabrication of low-cost active upper-limb prostheses. Such devices are usually controlled by surface electromyographic (sEMG) signals. Recently, portable, low-cost recording devices such as Thalmic Labs Myo Gesture Control Armband have been used in the movement detection. Such devices use undersampled sEMG signals. In this chapter, we discuss upper-limb prostheses and their control. We further provide the results of some experiments showing that such undersampled signals could be used for various applications required in advanced prosthesis control, e.g., force prediction, elbow angle prediction, movement detection, and time and frequency parameter extraction using undersampled sEMG signals. Finally, a low-cost controller for BRUNEL HAND 2.0 from Open Bionics is designed to link low-cost recording and prosthesis.This work was supported by the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R and the Ministry of Science and Innovation (MICINN), Spain, under contract PRE2018-085387. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. JF Alonso is a Serra Hunter Fellow. The research leading to this results has also received funding from the European's Union Horizon 2020 research and innovation program under the Marie Slodowska-Curie Grant Agreement No 712349 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.Postprint (published version