Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Heart rates estimation using rPPG methods in challenging imaging conditions

Abstract. The cardiovascular system plays a crucial role in maintaining the body’s equilibrium by regulating blood flow and oxygen supply to different organs and tissues. While contact-based techniques like electrocardiography and photoplethysmography are commonly used in healthcare and clinical monitoring, they are not practical for everyday use due to their skin contact requirements. Therefore, non-contact alternatives like remote photoplethysmography (rPPG) have gained significant attention in recent years. However, extracting accurate heart rate information from rPPG signals under challenging imaging conditions, such as image degradation and occlusion, remains a significant challenge. Therefore, this thesis aims to investigate the effectiveness of rPPG methods in extracting heart rate information from rPPG signals in these imaging conditions. It evaluates the effectiveness of both traditional rPPG approaches and rPPG pre-trained deep learning models in the presence of real-world image transformations, such as occlusion of the faces by sunglasses or facemasks, as well as image degradation caused by noise artifacts and motion blur. The study also explores various image restoration techniques to enhance the performance of the selected rPPG methods and experiments with various fine-tuning methods of the best-performing pre-trained model. The research was conducted on three databases, namely UBFC-rPPG, UCLA-rPPG, and UBFC-Phys, and includes comprehensive experiments. The results of this study offer valuable insights into the efficacy of rPPG in practical scenarios and its potential as a non-contact alternative to traditional cardiovascular monitoring techniques

Triboelectric Effect Enabled Self-Powered, Point-of-Care Diagnostics: Opportunities for developing ASSURED and REASSURED devices

The use of rapid point-of-care (PoC) diagnostics in conjunction with physiological signal monitoring has seen tremendous progress in their availability and uptake, particularly in low- and middle-income countries (LMICs). However, to truly overcome infrastructural and resource constraints, there is an urgent need for self-powered devices which can enable on-demand and/or continuous monitoring of patients. The past decade has seen the rapid rise of triboelectric nanogenerators (TENGs) as the choice for high-efficiency energy harvesting for developing self-powered systems as well as for use as sensors. This review provides an overview of the current state of the art of such wearable sensors and end-to-end solutions for physiological and biomarker monitoring. We further discuss the current constraints and bottlenecks of these devices and systems and provide an outlook on the development of TENG-enabled PoC/monitoring devices that could eventually meet criteria formulated specifically for use in LMICs.Ulster Universityhttp://www.mdpi.com/journal/micromachineshj2021Electrical, Electronic and Computer Engineerin