Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Facial videoplethysmography provides non-contact measurement of heart activity based on blood volume pulsations detected in facial tissue. Typically, the signal is extracted using a simple webcam followed by elaborated signal processing methods, and provides limited accuracy of time-domain characteristics. In this study, we explore the possibility of providing accurate time-domain pulse and inter-beat interval measurements using a high- quality image sensor camera and various signal processing approaches, and use these measurements to diagnose atrial fibrillation. We capture synchronized signals using a high- quality camera, a simple webcam, an earlobe photoplethysmography sensor, and a body- surface electrocardiogram from a large group of subjects, including subjects diagnosed with cardiac arrhythmias. All signals are processed using both blind source separation and color conversion. We then assess accuracy of IBI detection, heart rate variability estimation, and atrial fibrillation diagnose by comparing to a body-surface electrocardiogram. We present a new heart variability indicator for blood volume pulsating signals. Our results demonstrate that the accuracy of a facial VPG system is greatly improved when using a high-quality camera. Coupling the high-quality camera with color conversion from RGB to Hue provides a level of accuracy equivalent to that of commercially available photoplethysmography sensors, and offers a non-contact alternative to current technology for heart rate variability assessment and atrial fibrillation screening

A Deep Learning Classifier for Detecting Atrial Fibrillation in Hospital Settings Applicable to Various Sensing Modalities

Cardiac signals provide variety of information related to the patient\u27s health. One of the most important is for medical experts to diagnose the functionality of a patient’s heart. This information helps the medical experts monitor heart disease such as atrial fibrillation and heart failure. Atrial fibrillation (AF) is one of the most major diseases that are threatening patients’ health. Medical experts measure cardiac signals usng the Electrocardiogram (ECG or EKG), the Photoplethysmogram (PPG), and more recently the Videoplethysmogram (VPG). Then they can use these measurements to analyze the heart functionality to detect heart diseases. In this study, these three major cardiac signals were used with different classification methodologies such as Basic Thresholding Classifiers (BTC), Machine Learning (SVM) classifiers, and deep learning classifiers based on Convolutional Neural Networks (CNN) to detect AF. To support the work, cardiac signals were acquired from forty-six AF subjects scheduled for cardioversion who were enrolled in a clinical study that was approved by the Internal Review Committees to protect human subjects at the University of Rochester Medical Center (URMC, Rochester, NY), and the Rochester Institute of Technology (RIT, Rochester, NY). The study included synchronized measurements of 5 minutes and 30 seconds of ECG, PPG, VPG 180Hz (High-quality camera), VPG 30 Hz (low quality webcam), taken before and after cardioversion of AF subjects receiving treatment at the AF Clinic of URMC. These data are subjected to BTC, SVM, and CNN classifiers to detect AF and compare the result for each classifier depending on the signal type. We propose a deep learning approach that is applicable to different kinds of cardiac signals to detect AF in a similar manner. By building this technique for different sensors we aim to provide a framework to implement a technique that can be used for most devices, such as, phones, tablets, PCs, ECG devices, and wearable PPG sensors. This conversion of the different sensing platforms provides a single AF detection classifier that can support a complete monitoring cycle that is referring to screen the patient whether at a hospital or home. By using that, the risk factor of heart attack, stroke, or other kind of heart complications can be reduced to a low level to prevent major dangers, since increasing monitoring AF patients helps to predict the disease at an early stage as well as track its progress. We show that the proposed approach provides around 99% accuracy for each type of classifier on the test dataset, thereby helping generalize AF detection by simplifying implementation using a sensor-agnostic deep learning model

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system

Development and Validation of a Smartphone Heart Rate Acquisition Application for Health Promotion and Wellness Telehealth Applications

Objective. Current generation smartphones' video camera technologies enable photoplethysmographic (PPG) acquisition and heart rate (HR) measurement. The study objective was to develop an Android application and compare HRs derived from a Motorola Droid to electrocardiograph (ECG) and Nonin 9560BT pulse oximeter readings during various movement-free tasks. Materials and Methods. HRs were collected simultaneously from 14 subjects, ages 20 to 58, healthy or with clinical conditions, using the 3 devices during 5-minute periods while at rest, reading aloud under observation, and playing a video game. Correlation between the 3 devices was determined, and Bland-Altman plots for all possible pairs of devices across all conditions assessed agreement. Results. Across conditions, all device pairs showed high correlations. Bland-Altman plots further revealed the Droid as a valid measure for HR acquisition. Across all conditions, the Droid compared to ECG, 95% of the data points (differences between devices) fell within the limits of agreement. Conclusion. The Android application provides valid HRs at varying levels of movement free mental/perceptual motor exertion. Lack of electrode patches or wireless sensor telemetric straps make it advantageous for use in mobile-cell-phone-delivered health promotion and wellness programs. Further validation is needed to determine its applicability while engaging in physical movement-related activities

Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Widefield Computational Biophotonic Imaging for Spatiotemporal Cardiovascular Hemodynamic Monitoring

Cardiovascular disease is the leading cause of mortality, resulting in 17.3 million deaths per year globally. Although cardiovascular disease accounts for approximately 30% of deaths in the United States, many deleterious events can be mitigated or prevented if detected and treated early. Indeed, early intervention and healthier behaviour adoption can reduce the relative risk of first heart attacks by up to 80% compared to those who do not adopt new healthy behaviours. Cardiovascular monitoring is a vital component of disease detection, mitigation, and treatment. The cardiovascular system is an incredibly dynamic system that constantly adapts to internal and external stimuli. Monitoring cardiovascular function and response is vital for disease detection and monitoring. Biophotonic technologies provide unique solutions for cardiovascular assessment and monitoring in naturalistic and clinical settings. These technologies leverage the properties of light as it enters and interacts with the tissue, providing safe and rapid sensing that can be performed in many different environments. Light entering into human tissue undergoes a complex series of absorption and scattering events according to both the illumination and tissue properties. The field of quantitative biomedical optics seeks to quantify physiological processes by analysing the remitted light characteristics relative to the controlled illumination source. Drawing inspiration from contact-based biophotonic sensing technologies such as pulse oximetry and near infrared spectroscopy, we explored the feasibility of widefield hemodynamic assessment using computational biophotonic imaging. Specifically, we investigated the hypothesis that computational biophotonic imaging can assess spatial and temporal properties of pulsatile blood flow across large tissue regions. This thesis presents the design, development, and evaluation of a novel photoplethysmographic imaging system for assessing spatial and temporal hemodynamics in major pulsatile vasculature through the sensing and processing of subtle light intensity fluctuations arising from local changes in blood volume. This system co-integrates methods from biomedical optics, electronic control, and biomedical image and signal processing to enable non-contact widefield hemodynamic assessment over large tissue regions. A biophotonic optical model was developed to quantitatively assess transient blood volume changes in a manner that does not require a priori information about the tissue's absorption and scattering characteristics. A novel automatic blood pulse waveform extraction method was developed to encourage passive monitoring. This spectral-spatial pixel fusion method uses physiological hemodynamic priors to guide a probabilistic framework for learning pixel weights across the scene. Pixels are combined according to their signal weight, resulting in a single waveform. Widefield hemodynamic imaging was assessed in three biomedical applications using the aforementioned developed system. First, spatial vascular distribution was investigated across a sample with highly varying demographics for assessing common pulsatile vascular pathways. Second, non-contact biophotonic assessment of the jugular venous pulse waveform was assessed, demonstrating clinically important information about cardiac contractility function in a manner which is currently assessed through invasive catheterization. Lastly, non-contact biophotonic assessment of cardiac arrhythmia was demonstrated, leveraging the system's ability to extract strong hemodynamic signals for assessing subtle fluctuations in the waveform. This research demonstrates that this novel approach for computational biophotonic hemodynamic imaging offers new cardiovascular monitoring and assessment techniques, which can enable new scientific discoveries and clinical detection related to cardiovascular function

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed