Non-Contact Video Based Estimation for Heart Rate Variability Spectrogram using Ambient Light by Extracting Hemoglobin Information

The current definition of sinus tachycardia is a heart rate >100 beats per minute (bpm).1 This limit was set arbitrarily when heart rate was not yet regarded as a risk factor for cardiovascular disease, probably with the main purpose of distinguishing between a disease state (fever, thyrotoxicosis, anemia, congestive heart failure, etc) and a normal condition. In recent years, interest has been aroused by the awareness that fast heart rate is a potent precursor of hypertension, atherosclerosis, and their sequelae.2 3 4 5 6 7 8 9 10 In addition, many leading epidemiological studies have shown that tachycardia is associated with an increased risk of death from cardiovascular and noncardiovascular causes. This relationship has been found in general populations,3 4 5 6 7 in elderly individuals,9 and in hypertensive cohorts.10 In all of these studies, the heart rate value above that in which a significant increase in risk was observed was below the 100 bpm threshold (Table 1⇓). Only in the study by Levy et al3 was tachycardia defined as a heart rate >99 bpm, but in that study the cutoff between normal and high heart rate was chosen arbitrarily, and the highest heart rate value measured during the examination was taken to define the subject’s heart rate. In all the other studies, the threshold level between normal and fast heart rate was between 79 and 90 bpm. View this table: Table 1. Heart Rate Values Above Which a Significant Increase in Risk Was Found: Data From 9 Epidemiological Studies The normalcy limits of a clinical variable can be established according to different criteria. For many parameters, such as most biochemical indexes, the 95% confidence interval is calculated to identify the upper normal limit of the variable. This statistical approach does not appear suitable for those clinical variables …

Dissociation between ACE activity and autonomic response to ACE inhibition in patients with heart failure

Heart Rate Variability in Risk Stratification of Cardiac Patients

Type 2 diabetes mellitus has been associated with elevated high-sensitivity C-reactive protein (hsCRP), but the possible implication of ambulatory heart rate (HR) variability in enhancing chronic subclinical inflammation biomarkers remains to be elucidated. We aimed to evaluate the correlations between HR variability and coefficient of variance assessed during 24-hour ambulatory blood pressure monitoring (ABPM) and hsCRP in type 2 diabetes and control subjects. The observational study included type 2 diabetes (n=75) and control (n=11) subjects. HR variability was calculated as standard deviation of mean HR during daytime, nighttime and 24-hour periods. The coefficient of variation was calculated using standard deviation and mean HR. Nighttime HR dipping was calculated using daytime and nighttime HR variability. We found higher hsCRP in type 2 diabetes compared to controls. Also, we found that daytime, nighttime and 24-hour HR variability and coefficient of variance were lower in the type 2 diabetes group compared with the control group, while type 2 diabetes subjects receiving β-blockers had even lower ambulatory HR variability and coefficient of variance. Subjects with diabetic neuropathy, retinopathy and atherosclerotic cardiovascular disease had lower HR variability and coefficient of variance compared to their peers without disease. We observed that daytime and 24-hour HR variability inversely correlated with hsCRP, while all HR variability parameters inversely correlated with hypertension duration in the study population. Nighttime HR dipping inversely correlated with duration of type 2 diabetes and hypertension. Our results suggest that 24-hour ambulatory HR variability and coefficient of variance are significantly correlated with chronic inflammation evaluated using hsCRP in type 2 diabetes and control subjects, and these findings deserve further investigations.

Many patients with severe obstructive sleep apnea (OSA) do not complain of excessive daytime sleepiness (EDS), possibly due to increased sympathetic nervous activity (SNA) and accompanying heightened alertness. We hypothesized that in patients with OSA, those without subjective EDS (Epworth Sleepiness Scale, ESS score < 11) would have higher very low frequency (VLF) heart rate variability (HRV) during sleep, reflecting greater sympathetic heart rate modulation than patients with an ESS score ≥ 11. Patients with severe OSA (AHI ≥ 30: 26 with and 65 without heart failure) were divided into those with and without EDS. Heart rate (HR) signals were acquired in stage 2 sleep during periods of recurrent apneas and hypopneas and submitted to coarse graining spectral analysis, which extracts harmonic, neurally mediated contributions to HRV from total spectral power. Because the apnea-hyperpnea cycle entrains muscle SNA at VLF (0 to 0.04 Hz), VLF power was our principal between-group comparison. Subjects without EDS had higher harmonic VLF power (944 ± 839 vs 447 ± 461 msec(2), p = 0.003) than those with EDS, irrespective of the presence or absence of heart failure (1218 ± 944 vs 426 ± 299 msec(2), p = 0.043, and 1029 ± 873 vs 503 ± 533 msec(2), p = 0.003, respectively). ESS scores correlated inversely with VLF power in all (r = -0.294, p = 0.005) and in heart failure subjects (r = -0.468, p = 0.016). Patients with severe OSA but without EDS have higher VLF-HRV than those with EDS. This finding suggests that patients with severe OSA but without EDS have greater sympathetic modulation of HRV than those with EDS that may reflect elevated adrenergically mediated alertness. Taranto Montemurro L; Floras JS; Picton P; Kasai T; Alshaer H; Gabriel JM; Bradley TD. Relationship of heart rate variability to sleepiness in patients with obstructive sleep apnea with and without heart failure.

Rafael Correo, de izquierdas, y Alvaro Noboa, magnate conservador, vencieron con un discurso clientelista en el pais mas inestable de America Latina.

Imaging photoplethysmography (iPPG) technique has a bright prospect in noncontact heart rate (HR) and other types of vital signs detection; however, the iPPG method is highly susceptible to the motion artifacts in blood volume pulse (BVP) detected from facial video, and this is crucial for HR estimation. Although a number of signal processing algorithms regarding the resistance to motion interference have been proposed in recent years, their stability and computational complexity are hard to meet the requirements of practical applications. To address this problem, we suggest using the head-mounted camera (HMC) instead of a fixed position camera (FPC) in iPPG method because the HMC can move synchronously with the head, which can effectively alleviate the impact of motion artifacts. To this end, a prototype of HMC was designed and implemented. By referring to the results of CMS50E pulse oximeter synchronized with the subject's videos, the comparison study of HMC and FPC in iPPG-based HR estimation was conducted in three scenarios of resting, talking and head rotation. Furthermore, a HMC-based iPPG database named IPPG-HMC database was built. The experimental results show that HMC-based iPPG methods can deal with motion artifacts efficiently, and significantly improve the reliability and accuracy of HR estimation.

Remote photoplethysmography (rPPG) is a kind of video-based heart rate (HR) estimation technique which has widely potential applications in health monitoring and human- computer interaction. However, the accuracy of conventional rPPG methods is easily disturbed by motion and illumination artifacts. Recently, some deep learning based rPPG methods have attracted many attentions due to its good performance and robustness to noise. This paper proposes a new rPPG scheme using a convolutional neural network (CNN) to map the pulse accumulated image to corresponding true heart rate value, where the spatial-temporal input images are constructed with raw pulses from conventional rPPG methods. In order to check the feasibility and ideal performance of this method, synthetic rPPG pulses are built using real electrocardiograph (ECG) or blood volume pulse (BVP) signals through a modified Akima cubic Hermite interpolation. We test the proposed method in three cases, subject dependent, subject independent, and also a cross-dataset one. The experimental results show that our method performs well in heart rate value estimation with synthetic rPPG pulses even for the cross-dataset case (mean absolute error HR mae = 4.36 BPM, root mean square error HR rmse = 6.26 BPM, mean error rate percentage HR mer = 5.46%). This pilot study verifies the feasibility of the proposed method and provides a solid foundation for the follow-up research with real rPPG pulses.

With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers.

color images are usually viewed on CRT monitor screen under ambient light sources. The feeling for reproduced color on CRT monitor depends on white point of ambient light source and the CRT monitor screen. Therefore, if the effect of ambient light source is not considered, then the reproduced color does not match the intrinsic color. This paper proposes a method to investigate the effect of ambient light source on color appearance of a CRT monitor which is posited in a room illuminated by an experimental light source. The investigation for the effect of ambient light consists of two elements. One is the flare effect that the reflection on the CRT monitor screen which produced by the ambient light source. Another is the change of adaption in white point. When the images are viewed on a CRT monitor screen under ambient light, the sate of adaption is affected by both the white point of the monitor and ambient light source. Therefore, these two effects were analyzed in the experiments.

Debate has focused on the effects of the selective serotonin reuptake inhibitor (SSRI) antidepressants on heart rate (HR) and HR variability (HRV), both of which are predictors of adverse cardiovascular events. Here, we examine the associations between specific SSRI antidepressants and resting state HR (and HRV) after accounting for a host of potential confounding factors using propensity score techniques. Participants included 10,466 not taking antidepressants, 46 participants taking escitalopram, 86 taking citalopram, 66 taking fluoxetine, 103 taking paroxetine, and 139 taking sertraline. HR and HRV (root mean square of successive squared differences, high frequency) were extracted from 10-minute resting-state ECGs. Analyses including propensity score weighting and matching were conducted using R-statistics to control for potentially confounding variables. Major findings indicated that users of all SSRI medications-except fluoxetine-displayed lower HRV relative to nonusers. Users of paroxetine also displayed significantly lower HRV relative to users of citalopram (Cohen's d = 0.42), fluoxetine (Cohen's d = 0.54), and sertraline (Cohen's d = 0.35), but not escitalopram. Although associations were also observed for HR, these were less robust than those for HRV. Although paroxetine is associated with decreases in HRV relative to nonusers, as well as users of other SSRI medications, fluoxetine was the only medication not to display significant alterations in HR or HRV. These conclusions are limited by the cross-sectional design and nonrandomized nature of medication prescriptions. Findings highlight the importance of focusing on specific medications, rather than more heterogeneous groupings according to antidepressant action, and may have implications for health and well-being for the longer term.

The first (VT1) and second (VT2) ventilatory thresholds are landmarks to tailor exercise rehabilitation in coronary artery disease (CAD) or chronic heart failure (CHF). Methods allowing VT1 and VT2 determination based on heart rate variability (HRV) have been proposed but not yet evaluated in these patients. To determine the heart rate (HR) associated with VT1 and VT2 by three methods of HRV analyses. Fourteen CHF and 24 CAD patients performed an exercise test on a cycle ergometer (10 -W every minute until exhaustion). VT1 and VT2 were determined with the 'respiratory equivalent' method. HR at VT1 was determined with the standard deviation (SD) of R⊟R intervals (VT(SD)) and of the instantaneous beat-to-beat variability of the Poincaré plot method (VT(Poincaré)). HR at VT1 and VT2 was determined through a time-varying HRV analysis method (VT(TV1) and VT(TV2), respectively). HR at VT(SD) was significantly higher than HR at VT1. No significant differences were observed between HR at VT(Poincaré), VT(TV1), and at VT1, nor between HR at VT(TV2) and VT2. HR at VT(SD), VT(Poincaré), and VT1 were significantly correlated, but with a low r (2) value and a large mean HR difference. With the time-varying method, the mean HR difference was lower than 5% and the correlation coefficients were higher (especially for VT(TV2)). SD and Poincaré plot methods lead to substantial inaccuracy in HR estimates. The time-varying HRV analysis led to strong correlation coefficients and low limits of agreement. Therefore, this method may be a promising, low-cost tool for non-invasive assessment of the ventilatory thresholds in cardiac disease.

BackgroundAlthough electrocardiography is the gold standard for heart rate (HR) recording in clinical trials, the increasing availability of smartwatch-based HR monitors opens up possibilities for drug development studies. Smartwatches allow for inexpensive, unobtrusive, and continuous HR estimation for potential detection of treatment effects outside the clinic, during daily life.ObjectiveThe aim of this study is to evaluate the repeatability and sensitivity of smartwatch-based HR estimates collected during a randomized clinical trial.MethodsThe data were collected as part of a multiple-dose, investigator-blinded, randomized, placebo-controlled, parallel-group study of 12 patients with Parkinson disease. After a 6-day baseline period, 4 and 8 patients were treated for 7 days with an ascending dose of placebo and clenbuterol, respectively. Throughout the study, the smartwatch provided HR and sleep state estimates. The HR estimates were quantified as the 2.5th, 50th, and 97.5th percentiles within awake and asleep segments. Linear mixed models were used to calculate the following: (1) the intraclass correlation coefficient (ICC) of estimated sleep durations, (2) the ICC and minimum detectable effect (MDE) of the HR estimates, and (3) the effect sizes of the HR estimates.ResultsSleep duration was moderately repeatable (ICC=0.64) and was not significantly affected by study day (P=.83), clenbuterol (P=.43), and study day by clenbuterol (P=.73). Clenbuterol-induced changes were detected in the asleep HR as of the first night (+3.79 beats per minute [bpm], P=.04) and in the awake HR as of the third day (+8.79 bpm, P=.001). The median HR while asleep had the highest repeatability (ICC=0.70). The MDE (N=12) was found to be smaller when patients were asleep (6.8 bpm to 11.7 bpm) than while awake (10.7 bpm to 22.1 bpm). Overall, the effect sizes for clenbuterol-induced changes were higher while asleep (0.49 to 2.75) than while awake (0.08 to 1.94).ConclusionsWe demonstrated the feasibility of using smartwatch-based HR estimates to detect clenbuterol-induced changes during clinical trials. The asleep HR estimates were most repeatable and sensitive to treatment effects. We conclude that smartwatch-based HR estimates obtained during daily living in a clinical trial can be used to detect and track treatment effects.Trial RegistrationNetherlands Trials Register NL8002; https://www.trialregister.nl/trial/8002

The use of wearable photoplethysmography (PPG) technology for estimating heart rate (HR) and HR variability (HRV) in the health care system is gaining attention in recent years. However, the performance of these devices remains questionable in their ability to collect data in real working conditions for long‐term monitoring. The present study aimed to examine the data collected from nurses during working hours by PPG and electrocardiography (ECG) devices. Twenty‐two nurses underwent a 60‐minute work protocol during the normal working conditions while wearing a PPG device and an ECG device. HR, low‐frequency component (LF) and high‐frequency component (HF), LF/HF ratio, and percent LF distribution in total spectral power, and steps were examined. Pearson's correlation analysis and Bland‐Altman method was performed to examine the relationships between the two devices based on HR and HRV indices. The results found strong positive correlations between HR estimates of both devices, and moderate correlations between LF/HF ratio and percent LF indices estimates, respectively. Moreover, the Bland‐Altman analysis showed a small mean bias in general between the captured data of both devices. This pilot study suggested that the PPG device appears to demonstrate good overall reliability in measuring HR, LF/HF ratio, and percent LF. A further large‐scale study is required to investigate the feasibility and practicality for HR and HRV analysis in nurses during real working conditions using PPG devices.

Heart rate variability measures calculated from electrocardiography recordings reflect social competence. Clinical assessments of social skills have found that reduced heart rate variability is related to differences in the development of social skills in children and increase the risk of mental disorders. Limited by widespread manual signal processing and R-peak detection in current clinical assessments, most literature reports only short-term baseline studies, with fewer studies reporting social interaction settings with prolonged recording. There is an urgent need for an automated physiological signal processing toolbox to detect R-peaks and perform heart rate variability measurements in social settings. This paper proposes a modified automated Neurokit2 toolbox with signal processing procedures similar to the MindWare software that requires manual inspection of R-peak locations. We calculate time domain heart rate variability metrics from the publicly available QT database by PhysioNet collected at resting states and under stress tests, mimicking social interaction stress scenarios. Statistical analysis conveys that heart rate variability metrics calculation applying both signal processing approaches using the Neurokit2 toolbox are statistically equivalent in comparison to the hand-labelled R-peaks from the QT database (n= 10 in the normal sinus rhythm group, and n= 6 in the ST Change group). Such validation results are crucial for the adoption of automated toolboxes for heart rate variability measures in social interaction assessments, where more movement and mood changes of participants are expected.Clinical Relevance- This contributes to the body of evidence of the reliability of the Neurokit2 toolbox for automatic cleaning of prolonged cardiac electrophysiological signals and calculation of heart rate variability in time-domain characterization in social interaction stress assessment.