A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.Comment: Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020

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

Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm

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

The Abdominal Circulatory Pump

Blood in the splanchnic vasculature can be transferred to the extremities. We quantified such blood shifts in normal subjects by measuring trunk volume by optoelectronic plethysmography, simultaneously with changes in body volume by whole body plethysmography during contractions of the diaphragm and abdominal muscles. Trunk volume changes with blood shifts, but body volume does not so that the blood volume shifted between trunk and extremities (Vbs) is the difference between changes in trunk and body volume. This is so because both trunk and body volume change identically with breathing and gas expansion or compression. During tidal breathing Vbs was 50–75 ml with an ejection fraction of 4–6% and an output of 750–1500 ml/min. Step increases in abdominal pressure resulted in rapid emptying presumably from the liver with a time constant of 0.61±0.1SE sec. followed by slower flow from non-hepatic viscera. The filling time constant was 0.57±0.09SE sec. Splanchnic emptying shifted up to 650 ml blood. With emptying, the increased hepatic vein flow increases the blood pressure at its entry into the inferior vena cava (IVC) and abolishes the pressure gradient producing flow between the femoral vein and the IVC inducing blood pooling in the legs. The findings are important for exercise because the larger the Vbs the greater the perfusion of locomotor muscles. During asystolic cardiac arrest we calculate that appropriate timing of abdominal compression could produce an output of 6 L/min. so that the abdominal circulatory pump might act as an auxiliary heart

What is new in microcirculation and tissue oxygenation monitoring?

Ensuring and maintaining adequate tissue oxygenation at the microcirculatory level might be considered the holy grail of optimal hemodynamic patient management. However, in clinical practice we usually focus on macro-hemodynamic variables such as blood pressure, heart rate, and sometimes cardiac output. Other macro-hemodynamic variables like pulse pressure or stroke volume variation are additionally used as markers of fluid responsiveness. In recent years, an increasing number of technological devices assessing tissue oxygenation or microcirculatory blood flow have been developed and validated, and some of them have already been incorporated into clinical practice. In this review, we will summarize recent research findings on this topic as published in the last 2 years in the Journal of Clinical Monitoring and Computing (JCMC). While some techniques are already currently used as routine monitoring (e.g. cerebral oxygenation using near-infrared spectroscopy (NIRS)), others still have to find their way into clinical practice. Therefore, further research is needed, particularly regarding outcome measures and cost-effectiveness, since introducing new technology is always expensive and should be balanced by downstream savings. The JCMC is glad to provide a platform for such research