Modeling Blood Volume Pulse Signal Using Exercise Intensity

Objective. Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system. Most researchers employed deep learning IPPG algorithms to extract the blood volume pulse (BVP) signal, analyzing it predominantly through the heart rate (HR). However, this approach often overlooks the inherent intricate time-frequency domain characteristics in the BVP signal, which cannot be comprehensively deduced solely from HR. The analysis of HRV metrics through the BVP signal is imperative. Approach. In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC). Main results. The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively. Significance. This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.

This paper explores a novel method for anxiety detection in older adults using simple wristband sensors such as electrodermal activity (EDA) and photoplethysmogram (PPG) and a context-based feature. The proposed method for anxiety detection combines features from a single physiological signal with an experimental context-based feature to improve the performance of the anxiety detection model. The experimental data for this work are obtained from a year-long experiment on 41 healthy older adults (26 females and 15 males) in the age range 60–80 with mean age \(73.36 \pm 5.25\) during a Trier Social Stress Test (TSST) protocol. The anxiety level ground truth was obtained from State–Trait Anxiety Inventory (STAI), which is regarded as the gold standard to measure perceived anxiety. EDA and blood volume pulse (BVP) signals were recorded using a wrist-worn EDA and PPG sensor, respectively. 47 features were computed from EDA and BVP signal, out of which a final set of 24 significantly correlated features were selected for analysis. The phases of the experimental study are encoded as unique integers to generate the context feature vector. A combination of features from a single sensor with the context feature vector is used for training a machine learning model to distinguish between anxious and not-anxious states. Results and analysis showed that the EDA and BVP machine learning models that combined the context feature along with the physiological features achieved 3.37% and 6.41% higher accuracy, respectively, than the models that used only physiological features. Further, end-to-end processing of EDA and BVP signals was simulated for real-time anxiety level detection. This work demonstrates the practicality of the proposed anxiety detection method in facilitating long-term monitoring of anxiety in older adults using low-cost consumer devices.

Non-contact heart rate and heart rate variability measurements have applications in healthcare and affective computing. Recently, a system utilizing a five-band camera (RGBCO: red, green, blue, cyan, orange) was proposed, and shown to improve both remote measurement of heart rate and heart rate variability over an RGB camera. In this paper, we propose an improved method for video-based estimation of heart rate variability. We introduce three advancements over previous work utilizing five-band cameras: (1) an adaptive non-rectangular region of interest identified using automatically detected facial feature points, (2) improved peak detection within the blood volume pulse (BVP) signal, and (3) improved HRV calculation using the Welch periodogram. We apply our method to a test dataset of subjects at rest and under cognitive stress and show qualitative improvements in the stability of HRV spectrogram estimation. Although we evaluate our method using a five-band camera, the method could be applied to video recorded with any camera.

Remote photoplethysmography (rPPG) has shown great potential for heart rate variability (HRV) analysis due to its non-contact and convenient nature. However, HRV measurement relies on long-term and high-quality blood volume pulse (BVP) signals, which pose substantial challenges for existing rPPG techniques. To address this, we propose HRVFusion, a conditional diffusion model-based framework for HRV estimation from rPPG signals. In the forward diffusion process, raw chrominance (CHROM) signals extracted from a video are incorporated to simulate the complex noise disturbances typically observed in rPPG signals. During the reverse process, to enhance the model's sensitivity to HRVs, the high-precision BVP reconstruction is jointly guided using both the chrominance signal and its time-frequency ridge extracted via the wavelet synchrosqueezed transform (WSST). Furthermore, a convolution and mamba hybrid network is introduced to handle inputs of arbitrary length, meeting the requirements for long-term HRV analysis. A public dataset for long-term video-based HRV extraction, consisting of 15-minute recordings from 18 participants, is presented for the first time. Experimental results demonstrate that the proposed method achieves superior performance across multiple HRV metrics, with a mean absolute error (MAE) of 5-8 ms for SDNN, RMSSD below 18 ms, and Pearson correlation coefficients above 0.85 for frequency-domain indices (LF, HF, LF/HF), outperforming existing approaches. This study provides an accurate and robust non-contact solution for long-term video-based HRV measurement.

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.

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.

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.

AUTONOMIC AND HEMODYNAMIC ACTIVITY IN SEPSIS

Aim was to evaluate alterations of the non-linear short-term scaling exponent alpha1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability (HRV) as a sensitive marker for assessing global physiological demands during multiple running intervals. As a secondary analysis, agreement of ECG-derived respiratory frequency (EDR) compared to respiratory frequency (RF) derived from the metabolic cart was evaluated with the same chest belt device. Fifteen trained female and male long-distance runners completed four running bouts over 5 min on a treadmill at marathon pace. During the last 3 min of each bout gas exchange data and a single-channel ECG for the determination of HR, DFAa1 of HRV, EDR and RF were analyzed. Additionally, blood lactate concentration (BLC) was determined and rating of perceived exertion (RPE) was requested. DFAa1, oxygen consumption, BLC, and RPE showed stable behaviors comparing the running intervals. Only HR (p < 0.001, d = 0.17) and RF (p = 0.012, d = 0.20) indicated slight increases with small effect sizes. In addition, results point towards inter-individual differences in all internal load metrics. The comparison of EDR with RF during running revealed high correlations (r = 0.80, p < 0.001, ICC3,1 = 0.87) and low mean differences (1.8 ± 4.4 breaths/min), but rather large limits of agreement with 10.4 to -6.8 breaths/min. Results show the necessity of EDR methodology improvement before being used in a wide range of individuals and sports applications. Relationship of DFAa1 to other internal load metrics, including RF, in quasi-steady-state conditions bears the potential for further evaluation of exercise prescription and may enlighten decoupling mechanisms during prolonged exercise bouts.

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

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.

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 …

To evaluate in a general population, the relationships between dysglycaemia, insulin resistance and metabolic variables, and heart rate, heart rate recovery and heart rate variability. Four hundred and forty-seven participants in the Data from an Epidemiological Study on the Insulin Resistance syndrome (DESIR) study were classified according to glycaemic status over the preceding 9 years. All were free of self-reported cardiac antecedents and were not taking drugs which alter heart rate. During five consecutive periods: rest, deep breathing, recovery, rest and lying to standing, heart rate and heart rate varability were evaluated and compared by ANCOVA and trend tests across glycaemic classes. Spearman correlation coefficients quantified the relations between cardio-metabolic risk factors, heart rate and heart rate varability. Heart rate differed between glycaemic groups, except during deep breathing. Between rest and deep-breathing periods, patients with diabetes had a lower increase in heart rate than others (P(trend) < 0.01); between deep breathing and recovery, the heart rate of patients with diabetes continued to increase, for others, heart rate decreased (P(trend) < 0.009). Heart rate was correlated with capillary glucose and triglycerides during the five test periods. Heart rate variability differed according to glycaemic status, especially during the recovery period. After age, sex and BMI adjustment, heart rate variability was correlated with triglycerides at two test periods. Change in heart rate between recovery and deep breathing was negatively correlated with heart rate variability at rest, (r=-0.113, P < 0.05): lower resting heart rate variability was associated with heart rate acceleration. Heart rate, but not heart rate variability, was associated with glycaemic status and capillary glucose. After deep breathing, heart rate recovery was altered in patients with known diabetes and was associated with reduced heart rate variability. Being overweight was a major correlate of heart rate variability.

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