Heart Rate Variability Estimation Research Articles

Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep

Objective. This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology. Approach. HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland–Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described. Main results. HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) − Standing (−2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects. Significance. Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.

Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20-100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland-Altman plots and coefficient of variation (CoV). A sampling frequency of ~100-200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40-50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1-8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

P040 Comparisons of Heart Rate Variability Indices Derived from the Sheet-type Device beneath the Mattress with those from the Electrocardiogram

Abstract Introduction A sheet-type device beneath the mattress was developed for non-invasive monitoring of cardiac rhythm during sleep and in bedridden end-of-life elderly patients, enabling the acquisition of ballistocardiography (BCG) signals. BCG represents minute vibrations in the body associated with cardiac ejection of blood and has recently been utilized to estimate heart rate variability (HRV) indices. This study aimed to compare HRV indices derived from BCG with those obtained from simultaneously measured electrocardiogram (ECG) and assess their agreement. Methods Healthy subjects (14 males, 11 females; age 40.6±12.8 yrs.) were recruited between August 2022 and April 2023. Subjects laid on a bed with the device placed under their pillow, while ECG was recorded for 20 minutes. Time-domain indices (mean normal-to-normal (NN) interval) and frequency-domain indices (low frequency (LF) and high frequency (HF)) were calculated, and linear associations were examined by correlation analysis. Bland-Altman analysis was employed to evaluate the agreement between the indices. Results Significant positive associations were observed between BCG and ECG indices in mean NN interval (r=0.994, p<0.001) and LF (r=0.685, p<0.001) and HF (r=0.687, p<0.001). Bland-Altman analysis demonstrated good agreement in mean NN (mean of pairwise difference (MPD): 1.07; limits of agreement (LA): -12.57-14.70; ratio: 0.01), but poor agreement in LF (MPD: -4.32; LA: -30.42-21.78; ratio: 0.63) and HF (MPD: 4.29; LA: -21.70-30.28; ratio: 0.45) based on the ratios (> 0.2). Conclusion The high correlation and good agreement between mean NN intervals suggest that the BCG device can serve as an alternative to HRV for estimating NN interval.

Поражение сердца у юных элитных спортсменов, перенесших COVID-19

The impact of coronavirus infection (SARS-CoV-2) on cardiac output in underage athletes is uncertain. The study was aimed to determine heart disease in young elite athletes having a history of COVID-19 (SARS-CoV-2). A retrospective analysis of the results of the developed three-phase medical assessment of 236 elite athletes aged 14–17 (16 ± 1), who had had SARS-CoV-2 infection, was performed. The first phase of assessment involved examination, ECG, ECHO, bicycle ergometry (BEM), creatine kinase and creatine kinase MB tests. During the second phase 22 athletes (9.3%) underwent a more thorough assessment that included Holter monitoring (HM) with heart rate turbulence (HRT), microvolt T–wave alternans (MTWA), heart rate variability (HRV) estimation, high-resolution ECG (HRECG), determination of myocardial damage biochemical markers (troponin, NТproBNP) due to alterations revealed. Seven athletes (32%) having alterations revealed during this phase were referred to gadolinium enhancement cardiac magnetic resonance imaging (MRI) (the third phase). Myopericarditis was diagnosed in four cases (1.7% of 236) based on the results. Thus, low (below 2%) myocardial involvement has been revealed in young elite athletes, who have a history of SARSCoV-2 infection. Cardiovascular assessment algorithm has been developed for such athletes. Detection of cardiac arrhythmias by ECG, BEM, and HM is the most informative. HRECG, HRV, HRT, and MTWA can be used as additional methods to determine indications for MRI as a gold standard of the diagnosis of myocarditis.

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring.

Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.

Software Program for Analysis of HRV of Cardio Signals Registered through Electrocardiographic, Holter and Photopletysmographic Device

The article presents a demonstration software program for evaluating three types of cardiac signals. Mathematical methods for analyzing electrocardiographic, photoplethysmographic and Holter signals are considered. The different cardio signals are analyzed from the presented information system and conclusions are drawn regarding their practical use. The article describes software procedures for the analysis and estimation of heart rate variability extracted from the heart rate registration signals.

A Comprehensive Comparison of Six Publicly Available Algorithms for Localization of QRS Complex on Electrocardiograph.

The QRS complex is the most prominent feature of the electrocardiogram (ECG) that is used as a marker to identify the cardiac cycles. Identification of QRS complex locations enables arrhythmia detection and heart rate variability estimation. Therefore, accurate and consistent localization of the QRS complex is an important component of automated ECG analysis which is necessary for the early detection of cardiovascular diseases. This study evaluates the performance of six popular publicly available QRS complex detection methods on a large dataset of over half a million ECGs in a diverse population of patients. We found that a deep-learning method that won first place in the 2019 Chinese physiological challenge (CPSC-1) outperforms the remaining five QRS complex detection methods with an F1 score of 98.8% and an absolute sdRR error of 5.5 ms. We also examined the stratified performance of the studied methods on various cardiac conditions. All six methods had a lower performance in the detection of QRS complexes in ECG signals of patients with pacemakers, complete atrioventricular block, or indeterminate cardiac axis. We also concluded that, in the presence of different cardiac conditions, CPSC-1 is more robust than Pan-Tompkins which is the most popular model for QRS complex detection. We expect that this study can potentially serve as a guide for researchers on the appropriate QRS detection method for their target applications.Clinical Relevance-This study highlights the overall performance of publicly available QRS detection algorithms in a large dataset of diverse patients. We showed that there are specific cardiac conditions that are associated with the poor performance of QRS detection algorithms and may adversely influence the performance of algorithms that rely on accurate and reliable QRS detection.

Camera-based Cardiovascular Screening based on Heart Rate and Its Variability In Pre- and Post-Exercise Conditions.

Analysis of heart rate (HR) and heart rate variability (HRV) in pre- and post-exercise conditions can provide useful information about the health condition of cardiovascular system. Remote photoplethysmography (rPPG) that uses a contactless camera to measure vital signs from the human face might allow for ubiquitous applications in the fitness scenario. This paper benchmarks the accuracy of HR and HRV measured by ECG, PPG and rPPG, and investigates the difference of HR and HRV parameters between individuals with/without the exercise habit in pre- and post-exercise phases. We built a fitness benchmark consisting of 14 healthy subjects to perform running exercise on a treadmill, with video and reference data recorded simultaneously. The results show that rPPG has a similar performance as PPG in both the estimation of HR and HRV. The HRV parameters (Mean IBI, SDNN, LF, VLF, and SD2) of rPPG/PPG show good agreements with the ECG reference. Subjects with a regular exercise habit show lower resting HR and smaller values of HRV parameters in pre-exercise phase, fewer changes in HR and HRV at the same exercise intensity, and faster HR recovery after exercising, as compared with those without an exercise habit. The preliminary results suggest that rPPG is a promising surrogate of PPG for screening HR and HRV before and after exercise, to indicate the performance of cardiac training.

Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization

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.

Neurophysiological changes associated with vibroacoustically-augmented breath-focused mindfulness for dissociation: targeting interoception and attention.

Dissociative symptoms can emerge after trauma and interfere with attentional control and interoception; disruptions to these processes are barriers to mind-body interventions such as breath-focused mindfulness (BFM). To overcome these barriers, we tested the use of an exteroceptive augmentation to BFM, using vibrations equivalent to the amplitude of the auditory waveform of the actual breath, delivered via a wearable subwoofer in real time (VBFM). We tested whether this device enhanced interoceptive processes, attentional control and autonomic regulation in trauma-exposed women with dissociative symptoms. 65 women, majority (82%) Black American, aged 18-65 completed self-report measures of interoception and 6 BFM sessions, during which electrocardiographic recordings were taken to derive high-frequency heart rate variability (HRV) estimates. A subset (n = 31) of participants completed functional MRI at pre- and post-intervention, during which they were administered an affective attentional control task. Compared to those who received BFM only, women who received VBFM demonstrated greater increases in interoception, particularly their ability to trust body signals, increased sustained attention, as well as increased connectivity between nodes of emotion processing and interoceptive networks. Intervention condition moderated the relationship between interoception change and dissociation change, as well as the relationship between dissociation and HRV change. Vibration feedback during breath focus yielded greater improvements in interoception, sustained attention and increased connectivity of emotion processing and interoceptive networks. Augmenting BFM with vibration appears to have considerable effects on interoception, attention and autonomic regulation; it could be used as a monotherapy or to address trauma treatment barriers.

Sex Differences in Heart Rate Variability during Metaboreflex Activation

Women have been shown to experience blunted metaboreflex activation compared to men (Jarvis et al., 2011; Joshi & Edgell, 2019; Minahan et al., 2018; Samora et al., 2020), which may derive from altered autonomic balance. Since heart rate variability (HRV) estimates sympathetic and parasympathetic outflow, this study aims to determine if there are sex differences in HRV during metaboreflex activation. High frequency (HF) power and greater RR interval variability (SDRR) represent cardiac parasympathetic outflow, whereas the ratio between low frequency (LF) and HF power (LF/HF) represents cardiac sympathetic outflow. Thirty-two young, healthy men (n=16) and women (n=16) were recruited to perform 2mins of isometric handgrip at 40% of their maximal voluntary contraction followed by 3mins of post-exercise circulatory occlusion (PECO). ECG was continuously recorded, and HRV analysis was determined using 3mins of ECG at baseline or PECO. In response to PECO, heart rate (Women 71±12bpm vs. 73±13bpm; Men 68±12bpm vs. 72±13bpm; p=0.026) and mean arterial pressure (Women 84±8mmHg vs. 93±10mmHg; Men 85±9mmHg vs. 97±11mmHg; p<0.001) increased equally in both sexes. There were no significant differences in SDRR, root mean square standard deviation, and total power due to sex or PECO (all p>0.07). At all timepoints and compared to men, women had 1) significantly lower LF power (Women 19±10nu vs. 24±16nu; Men 42±21nu vs. 49±17nu; p<0.001), 2) higher HF power (Women 78±9nu vs. 74±16nu; Men 57±20nu vs. 50±17nu; p<0.001), 3) higher proportion of RR intervals differences greater than 50ms (Women 60±21% vs. 58±22%; Men 39±16% vs. 35±20%; p=0.003), regardless of time (p>0.2), and 4) lower LF/HF (Women 0.26±0.17 vs. 0.41±0.42; Men 1.01±0.90 vs. 1.34±1.30; p=0.002). Unlike previous findings, no sex differences were observed in the pressor response to metaboreflex activation. HRV did not differ in response to PECO in both sexes; however, women had greater cardiac parasympathetic dominance than men. Funding provided by Natural Sciences and Engineering Research Council of Canada This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Complex assessment of parameters of central hemodynamics and microcirculation in children of 6-7 years old

The paper presents the results of a comprehensive assessment of central hemodynamic and microcirculation indices in the skin of upper and lower limbs in 6–7-year-old children. 14 girls and 7 boys were examined using laser Doppler flowmetry methods (the LASMA-PF device, allowing simultaneous recording by four sensors) and the analysis of heart rate variability ("Varicard"). The parameters characterizing microcirculation in the limbs were determined and the signs of the appearing functional asymmetry of the upper limbs, as well as the differences in the mechanisms of modulation of cutaneous blood flow in boys and girls were revealed. The estimation of heart rate variability reveals the signs of girls' inclination to sympathotony and boys' inclination to vagotony at the given age. It was marked that central regulation mechanisms have the greater contribution to cardiac rhythm formation in girls. The data obtained testify to the fact that age-related changes at different levels of the cardiovascular system and regulation mechanisms occur somewhat earlier in girls than in boys.

On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals

Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used.

Abstract 14985: Artificial Intelligence Based Heart Rate Variability Analysis for Sudden Cardiac Arrest Risk Prediction

Introduction: Sudden Cardiac Arrest (SCA) is an unexpected life-threatening loss of cardiac function potentially leading to sudden cardiac death (SCD). It accounts for a half of cardiac mortality, posing a major public health problem. While SCA can be treated with implantable cardioverter defibrillators (ICD), most (80%) SCDs occur in relatively low risk patients, for whom risk benefit ratio of an ICD is unfavorable. Therefore, novel methods to identify high risk individuals and the time on which their risk is high may facilitate prevention of SCD. Methods : Long (~24h) ECG recordings from individuals who had an SCA on record (n=20), patients with other arrhythmias (n=84) and from healthy individuals (n=17) from PhysioNet databases were analyzed to extract inter beat intervals (IBI). For each step in the IBI series, 120 last IBIs were used to calculate 19 indices that quantify short term heart rate variability (HRV). An artificial intelligence model named temporal convolutional network (TCN) was used to continuously assess the risk of a future VF event based on the current HRV estimate combined with few hours of history of previous HRV estimates. Training and testing of the TCN was done using Leave One Out testing done twice. Patients being classified at any point in the record as high risk were considered high risk. Results : Only patients from the SCA population, and none from the other arrythmia and healthy patients, were classified as high risk (PPV=100%). On average, sensitivity and specificity were 62.5% and 100% respectively Conclusions : In this preliminary retrospective analysis, artificial intelligence based HRV analysis identified risk for a SCA event in the following 24 hours with high specificity. Larger cohort and prospective testing are needed to assess the clinical utility of such systems.

Effect of breathing intervention in patients with COVID and healthcare workers.

BackgroundRegulated breathing facilitates ventilation and reduces breathlessness. However, the effect of Yogic breathing on patients with COVID remains unclear. We aimed to evaluate the efficacy of two breathing protocols, i.e., short breathing technique (SBT) and long duration breathing technique (LBDT).MethodsThree groups including COVID-positive patients, COVID-recovered patients, and healthcare workers (HCWs) were included in the study and segregated into Yoga and control groups. SBT was administered to COVID-positive patients. Both SBT and LBDT were administered to COVID-recovered patients and HCWs. A total of 18 biochemical parameters, a 6-min walk test (6MWT), and a 1-min sit-stand test (1MSST) were assessed on 0th, 7th, and 15th days, where biochemical parameters were the primary outcome. Pre-post estimation of neuropsychological parameters (nine questionnaires) and heart rate variability (HRV) were carried out. The paired t-test or Wilcoxon rank test was applied for pre-post comparison and the Student's t-test or Mann–Whitney U test was used for group comparison. Repeated measures test was applied for data recorded at three time points.ResultsA significant elevation in white blood cell (WBC) count was observed in COVID-positive intervention (p < 0.001) and control groups (p = 0.003), indicating no role of intervention on change in WBC number. WBC count (p = 0.002) and D-dimer (p = 0.002) significantly decreased in the COVID-recovered intervention group. D-dimer was also reduced in HCWs practicing Yogic breathing as compared to controls (p = 0.01). D-dimer was the primary outcome, which remained below 0.50 μg/ml (a cutoff value to define severity) in the COVID-positive yoga group (CYG) and decreased in the COVID-recovered yoga group (RYG) and the HCW yoga group (HYG) after intervention. A 6-min walk test (6MWT) showed an increase in distance covered among the COVID-positive patients (p = 0.01) and HCWs (p = 0.002) after intervention. The high-frequency power (p = 0.01) was found to be reduced in the COVID-positive intervention group. No significant change in neuropsychological parameters was observed.ConclusionYogic breathing lowered D-dimer, which is helpful in reducing thrombosis and venous thromboembolism in patients with COVID-19 besides lowering the chances of vaccine-induced thrombotic thrombocytopenia in vaccinated individuals. The breathing intervention improved exercise capacity in mild to moderate cases of COVID-19. Further studies can show if such breathing techniques can influence immunity-related genes, as reported recently in a study. We suggest that Yogic breathing may be considered an integrative approach for the management of patients with COVID.Trial registrationhttp://ctri.nic.in/Clinicaltrials/login.php, identifier: CTRI/2020/10/028195.

Contactless Monitoring of PPG Using Radar

In this paper, we propose VitaNet, a radio frequency based contactless approach that accurately estimates the PPG signal using radar for stationary participants. The main insight behind VitaNet is that the changes in the blood volume that manifest in the PPG waveform are correlated to the physical movements of the heart, which the radar can capture. To estimate the PPG waveform, VitaNet uses a self-attention architecture to identify the most informative reflections in an unsupervised manner, and then uses an encoder decoder network to transform the radar phase profile to the PPG sequence. We have trained and extensively evaluated VitaNet on a large dataset obtained from 25 participants over 179 full nights. Our evaluations show that VitaNet accurately estimates the PPG waveform and its derivatives with high accuracy, significantly improves the heart rate and heart rate variability estimates from the prior works, and also accurately estimates several useful PPG features. We have released the codes of VitaNet as well as the trained models and the dataset used in this paper.

Toward Nonintrusive Camera-Based Heart Rate Variability Estimation in the Car Under Naturalistic Condition

Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of the existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their performance on the detection of HR does not guarantee a reliable computation of the HR variability (HRV) measure, which is a more applicable metric for inferring mental and physiological status. The accurate computation HRV measure is based on the precise measurement of the beat-to-beat interval, which can only be accomplished by medical-grade devices that attach electrodes to the body. Considering these existing challenges, we proposed a facial expression-based HRV estimation solution. The rationale is to establish a link between facial expression and heartbeat since both are controlled by the autonomic nervous system. To solve this problem, we developed a tree-based probabilistic fusion neural network approach, which significantly improved HRV estimation performance compared to conventional random forest or neural network methods and the measurements from smartwatches. The proposed solution relies only on commodity camera with a lightweighted algorithm, facilitating its ubiquitous deployment in current and future vehicles. Our experiments are based on 3400 km of driving data from nine drivers collected in a naturalistic field study.

Peak Detection and HRV Feature Evaluation on ECG and PPG Signals

Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.