Assessment of ROI Selection for Facial Video-Based rPPG

In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm.

Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals

Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.

Relationships between cardiovascular signs and neurological signs in asphyxiated neonates in Ilorin, North Central Nigeria

Background: Perinatal asphyxia is a condition associated with multiple organ dysfunctions inclusive of cardiovascular dys- function. Neurologic predictors of survival have been studied, but little has been reported regarding cardiovascular signs and their role in determining outcome in asphyxia. Objective: The study aimed to determine the relationship between cardiovascular signs and outcomes in asphyxiated new- borns with hypoxic ischaemic encephalopathy. Methods: This was a cross sectional study involving asphyxiated new-born babies recruited within the first 24 hours of life. Hypoxic ischaemic encephalopathy staging was done using Sarnat and Sarnat staging. All patients had a detailed cardi- ovascular examination on admission, after initial resuscitation (30 – 60 minutes) into admission, and were followed till final outcome: discharge or death. Results: Eighty-five asphyxiated new-borns with HIE were studied over seven months. Abnormal cardiovascular-related signs identified in the patients included respiratory distress (64.7%), small volume pulse (57.6%), hypotension (52.9%), hy- poxemia (48.2%) and shock (32.9%). Five babies died. None of the clinical signs had a significant relationship with mortality. Conclusion: Abnormal cardiovascular signs increased with the progression of HIE staging but had no relationship with mortality. Keywords: Cardiovascular signs; Neurological outcomes; Mortality; Perinatal asphyxia.

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO2 in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

Automated, high-accuracy, remote measurement of heart rate

Arrhythmia is a marked symptom of many cardiovascular diseases. The accurate and in time detection of heart rate can greatly reduce its harm to people. However, it is still a challenge to automatedly and remotely measure the heart rate in daily life, because the environment factor of the measurement changes variously, such as the changing light intensity, the movement of people, and the uncertain distance from sensor to people. In this study, we accurately measured the heart rate of people at the distance of 4.8 meters under different intensity of light just by using a surveillance camera. After a short color video (20 sec) of a person's hand was captured by this camera, a method based on Fast Fourier Transform Algorithm (FFT) is proposed to extract the blood volume pulse wave to calculate the heart rate. By comparing the real heart rate with the results measured by electrocardiography (ECG), the accuracy of heart rate measurement using the method proposed in this study is 98.65% within 4.0 meters, and the accuracy can reach 90% within 5.6 meters. Our experiments also demonstrated that this method can accurately obtain the heart rate even when the intensity of light is below 32 LUX ( office environment 300-500 LUX). The strong environmental suitability makes this method can be applied to many occasions, such as community clinic, old peoples' home, classroom, and other public space.

325 Stress-Induced Physiological Profiles Among Different Phenotype Insomniacs

Introduction Stress reactivity and autonomic nervous system (ANS) dysregulation have been suggested to be the pathophysiology of insomnia. Based on the finding PSG-measured short sleep duration was associated with higher morbidity of metabolic and cardiovascular disease. Vgontzas and Fernandez-Mandoza (2013) proposed that objective sleep duration is a biomarker for insomnia phenotypes. The phenotype with short objective sleep duration is associated with increased stress-related physiological hyperarousal. The present study aims to test this hypothesis by comparing the stress-induced cardiovascular reactivity between insomnia patients with short and long objective sleep durations. Methods 27 insomnia patients (age mean 34.48 ±12.87, Male: Female= 6:21) without comorbidity of psychiatric, medical or sleep disorders participated in this study. They went through one night of 8-hour PSG recording and were divided into two groups by their total sleep time with a cutoff of 6 hours. Nine participants were in short sleep duration group and 18 in longer sleep duration group. Psychophysiological reactivity profile, as recorded with EKG, skin conductance (SC), body temperature (BT), blood volume pulse (BVP), respiration rate (RR), was measured under three conditions: baseline resting state, arithmetic word problems solving, and recovery resting state. Results Both groups showed similar stress physiological response with increased heart rate (HR) and SC, nearly equivalent BT and BVP, and decreased RR when solving arithmetic problems, and opposite reaction during recovery resting state. Mann-Whitney U test comparing the changes from baseline resting state on all the psychophysiological measures between two phenotypes of insomnia showed no significant differences: stress-induced heart-rate (U=106, p=.119.) recovery heart-rate (U=44, p=.095), stress-induced skin conductance (U=104.5, p=.132),recovery skin conductance (U=51.5, p=.198), stress-induced body temperature (U=79, p=.897),recovery body temperature (U=60.5, p=.418), stress-induced blood volume pulse amplitude (U=77, p=1.0), and recovery blood volume pulse amplitude (U=69, p=.735), stress-induced respiration rate (U=76, p =.696), and recovery respiration rate (U=85, p=.658). Conclusion Our results indicate that the insomnia phenotypes with short and long objective sleep duration are not different in their stress-induced physiological responses. Future studies are needed to confirm these results and to explore other mechanisms for the increased metabolic and cardiovascular disease risk in insomnia patients with short objective sleep duration. Support (if any):