How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device

In clinical conditions, polysomnography (PSG) is regarded as the "golden standard" for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into "good sleep" and "poor sleep" based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F1-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring.

Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study

BACKGROUND

Wearable devices collect physiological and behavioral data that have the potential to identify individuals at risk of declining mental health and well-being. Past research has mainly focused on assessing the accuracy and the agreement of heart rate (HR) measurement of wearables under different physical exercise conditions. However, the capacity of wearables to sense physiological changes, assessed by increasing HR, caused by a stressful event has not been thoroughly studied.

OBJECTIVE

This study followed 3 objectives: (1) to test the ability of a wearable device (Fitbit Versa 2) to sense an increase in HR upon induction of psychological stress in the laboratory; (2) to assess the accuracy of the wearable device to capture short-term HR variations caused by psychological stress compared to a gold-standard electrocardiogram (ECG) measure (Biopac); and (3) to quantify the degree of agreement between the wearable device and the gold-standard ECG measure across different experimental conditions.

METHODS

Participants underwent the Trier Social Stress Test protocol, which consists of an oral phase, an arithmetic stress phase, an anticipation phase, and 2 relaxation phases (at the beginning and the end). During the stress protocol, the participants wore a Fitbit Versa 2 and were also connected to a Biopac. A mixed-effect modeling approach was used (1) to assess the effect of experimental conditions on HR, (2) to estimate several metrics of accuracy, and (3) to assess the agreement: the Bland-Altman limits of agreement (LoA), the concordance correlation coefficient, the coverage probability, the total deviation index, and the coefficient of an individual agreement. Mean absolute error and mean absolute percent error were calculated as accuracy indices.

RESULTS

A total of 34 university students were recruited for this study (64% of participants were female with a mean age of 26.8 years, SD 8.3). Overall, the results showed significant HR variations across experimental phases. Post hoc tests revealed significant pairwise differences for all phases. Accuracy analyses revealed acceptable accuracy according to the analyzed metrics of accuracy for the Fitbit Versa 2 to capture the short-term variations in psychological stress levels. However, poor indices of agreement between the Fitbit Versa 2 and the Biopac were found.

CONCLUSIONS

Overall, the results support the use of the Fitbit Versa 2 to capture short-term stress variations. The Fitbit device showed acceptable levels of accuracy but poor agreement with an ECG gold standard. Greater inaccuracy and smaller agreement were found for stressful experimental conditions that induced a higher HR. Fitbit devices can be used in research to measure HR variations caused by stress, although they cannot replace an ECG instrument when precision is of utmost importance.

Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings

BACKGROUND

This study aimed to investigate the role of a time-domain Heart Rate Variability index (the root mean square of successive difference between NN intervals, rMSSD) as a predictor of the onset of postpartum depression. HRV has been related to an increased risk of depression in the general population. However, its role in pregnant women is not clear, and the potential use of smartphone applications to evaluate HRV in this population has not been investigated.

METHODS

In study 1, simultaneous electrocardiogram and smartphone photoplethysmography were collected. The rMSSD was determined from each recording to evaluate the accuracy of a smartphone application in the measurement of HRV. In study 2, 135 pregnant women provided rMSSD values measured through a smartphone application in the prepartum (second or third trimester) and filled in the Edinburgh Postnatal Depression Scale in the postpartum (one month after the childbirth).

RESULTS

Study 1 showed the excellent accuracy of the smartphone application in the measurement of rMSSD. Study 2 indicated that lower prepartum rMSSD predicted higher depressive symptoms in the postpartum (β = -0.217, p = 0.010) after controlling for prepartum depressive symptoms and other potential covariates.

LIMITATIONS

Artefacts (e.g., hand movements) might have corrupted the physiological signal registered.

CONCLUSION

This study showed that a reduced vagal tone, indexed by lower rMSSD, during pregnancy was a predictor of depressive symptoms one month after childbirth. The prepartum period may offer an important timeframe to implement preventive intervention on vagal modulation in order to prevent depressive symptoms in the postpartum.

Spectral analysis for pulse rate variability assessment from simulated photoplethysmographic signals

Introduction: Pulse rate variability (PRV) refers to the changes in pulse rate through time and is extracted from pulsatile signals such as the photoplethysmogram (PPG). Although PRV has been used as a surrogate of heart rate variability (HRV), which is measured from the electrocardiogram (ECG), these variables have been shown to have differences, and it has been hypothesised that these differences may arise from technical aspects that may affect the reliable extraction of PRV from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals. Aim: In this study, the extraction of frequency-domain information from PRV was studied, in order to establish the best performing combination of parameters and algorithms to obtain the spectral representation of PRV. Methods: PPG signals with varying and known PRV content were simulated, and PRV information was extracted from these signals. Several spectral analysis techniques with different parameters were applied, and absolute, relative and centroid-related frequency-domain indices extracted from each combination. Indices from extracted and known PRV were compared using factorial analyses and Kruskal-Wallis tests to determine which spectral analysis technique gave the best performing results. Results: It was found that using fast Fourier transform and the multiple signal classification (PMUSIC) algorithms gave the best results, combined with cubic spline interpolation and a frequency resolution of 0.0078 Hz for the former; and a linear interpolation with a frequency resolution as low as 1.22 × 10^-4, as well as applying a fifth order model, for the latter. Discussion: Considering the lower complexity of FFT over PMUSIC, FFT should be considered as the appropriate technique to extract frequency-domain information from PRV signals.

The Dosage Effect of Laser Acupuncture at PC6 (Neiguan) on Heart Rate Variability: A Pilot Study

Laser acupuncture (LA) has been more applicated in the clinical practice with good responses, but the dosage and parameter settings are still inconsistent with the arguments. This study is focused on the effect of LA on heart rate variability (HRV) with different energy density (ED). Based on the Arndt-Schulz law, we hypothesized that the effective range should fall within 0.01 to 10 J/cm² of ED, and settings above 10 J/cm² would perform opposite or inhibitory results. We recruited healthy adults in both sexes as subjects and choose bilateral PC6 (Neiguan) as the intervention points to observe the HRV indexes changes by an external wrist autonomic nerve system (ANS) watch on the left forearm. The data from the ANS watch, including heart rate, blood pressure, and ANS activity indexes, such as low frequency (LF), high frequency (HF), LF%, HF%, LF/HF ratio, and so on, were analyzed by the one-way ANOVA method to test the possible effect. In this study, every subject received all three different EDs of LA in a randomized order. After analyzing the data of 20 subjects, the index of HF% was upward and LF/HF ratio was downward when the ED was 7.96 J/cm². Otherwise, the strongest ED 23.87 J/cm² performed the opposite reaction. Appropriately, LA intervention could affect the ANS activities, with the tendency to increase the ratio of parasympathetic and decrease the ratio of sympathetic nerve system activities with statistically significant results, and different ED interventions are consistent with Arndt-Schulz law with opposite performance below and above 10 J/cm².

Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network

Hypertension is a severe public health issue worldwide that significantly increases the risk of cardiac vascular disease, stroke, brain hemorrhage, and renal dysfunction. Early screening of blood pressure (BP) levels is essential to prevent the dangerous complication associated with hypertension as the leading cause of death. Recent studies have focused on employing photoplethysmograms (PPG) with machine learning to classify BP levels. However, several studies claimed that electrocardiograms (ECG) also strongly correlate with blood pressure. Therefore, we proposed a concatenated convolutional neural network which integrated the features extracted from PPG and ECG signals. This study used the MIMIC III dataset, which provided PPG, ECG, and arterial blood pressure (ABP) signals. A total of 14,298 signal segments were obtained from 221 patients, which were divided into 9150 signals of train data, 2288 signals of validation data, and 2860 signals of test data. In the training process, five-fold cross-validation was applied to select the best model with the highest classification performance. The proposed concatenated CNN architecture using PPG and ECG obtained the highest test accuracy of 94.56-95.15% with a 95% confidence interval in classifying BP levels into hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2. The result shows that the proposed method is a promising solution to categorize BP levels effectively, assisting medical personnel in making a clinical diagnosis.

Overnight pulse rate variability and risk of major neurocognitive disorder in older patients with obstructive sleep apnea

BACKGROUND

Increasing evidence links obstructive sleep apnea (OSA) to cognitive decline. Autonomic dysfunction assessed by heart rate variability is a promising early biomarker of cognitive impairment in populations without major neurocognitive disorder (MND). We aimed to determine whether nocturnal pulse rate variability (PRV) extracted from oximetry signal and OSA severity could predict MND onset among older OSA patients.

METHODS

This study relied on data collected within the multicenter longitudinal Pays de la Loire Sleep Cohort, linked to health administrative data to identify new-onset MND. We included patients ≥60 years with newly diagnosed OSA, and no history of MND or atrial fibrillation. Cox proportional-hazards models were used to evaluate the association of MND with indices of PRV and OSA severity generated from sleep recordings.

RESULTS

After a median follow-up of 6.8 [4.7-9.4] years, 70 of 3283 patients (2.1%) had been diagnosed with MND. In multivariable Cox models, MND incidence was associated with age (p < 0.0001), depression (p = 0.013), and PRV assessed by the root mean square of the successive normal-to-normal (NN) beat interval differences (RMSSD; p = 0.008) and standard deviation of NN beat intervals (SDNN; p = 0.02). Patients with the highest quartile of RMSSD had a 2.3-fold [95%CI 1.11-4.92] higher risk of being diagnosed with MND. Indices of OSA and nocturnal hypoxia severity were not associated with MND.

CONCLUSIONS

Within a large clinic-based cohort of older patients with OSA, we found an association between oximetry-based indices of PRV and the onset of MND. Nocturnal oximetry-derived PRV indices could allow the early identification of OSA patients at higher risk of MND.

Changes in Nutritional State and Cardiovascular Parameters in Alimentary Obese Children after a Month-Long Stay in Children's Treatment Center

Childhood and adolescent obesity has become an important public health issue, as it leads to higher risk of cardio−metabolic, orthopedic, and psychological comorbidities. The aim of this study was to evaluate the changes in nutritional state and cardiovascular system parameters in obese children. Sixty respondents aged 9−17 years with alimentary obesity participated in this research. Anthropometric parameters (body weight (BWT), body mass index (BMI), percentage of body fat (%), waist and hip circumference (WC and HC), waist−hip ratio (WHR)) and cardiovascular parameters (systolic and diastolic blood pressure (SP and DP), cardio-ankle vascular index (CAVI), ankle-brachial index (ABI), pulse wave velocity and its variability (PWV and PWVV), and parameters of pulse wave analysis) were measured. Every respondent went through two sets of measurements, the first (I.) after their admission to the children’s hospital and the second (II.) at the end of their one-month-long therapeutic stay. Statistically significant differences between measurements I. and II. were observed in the following parameters: BWT (p < 0.01), BMI (p < 0.01), WC (p < 0.01), HC (p < 0.01), DP (p < 0.01), PWV (p < 0.05), and ABI (p < 0.01). The results of this study show that obesity has a mostly negative impact on the cardiovascular health of affected children, with likely negative results in their adulthood.

Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations

Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application.

Accurate detection of heart rate using in-ear photoplethysmography in a clinical setting

Background

Recent research has shown that photoplethysmography (PPG) based wearable sensors offer a promising potential for chronic disease monitoring. The aim of the present study was to assess the performance of an in-ear wearable PPG sensor in acquiring valid and reliable heart rate measurements in a clinical setting, with epileptic patients.

Methods

Patients undergoing video-electroencephalography (EEG) monitoring with concomitant one-lead electrocardiographic (ECG) recordings were equipped with an in-ear sensor developed by cosinuss°.

Results

In total, 2,048 h of recording from 97 patients with simultaneous ECG and in-ear heart rate data were included in the analysis. The comparison of the quality-filtered in-ear heart rate data with the reference ECG resulted in a bias of 0.78 bpm with a standard deviation of ±2.54 bpm; Pearson’s Correlation Coefficient PCC = 0.83; Intraclass Correlation Coefficient ICC = 0.81 and mean absolute percentage error MAPE = 2.57.

Conclusion

These data confirm that the in-ear wearable PPG sensor provides accurate heart rate measurements in comparison with ECG under realistic clinical conditions, especially with a signal quality indicator. Further research is required to investigate whether this technology is helpful in identifying seizure-related cardiovascular changes.

Effects of Missing Data on Heart Rate Variability Metrics

Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

Evidence for engagement of the nucleus of the solitary tract in processing intestinal chemonociceptive input irrespective of conscious pain response in healthy humans

ABSTRACT

Neuroimaging studies have revealed important pathomechanisms related to disorders of brain-gut interactions, such as irritable bowel syndrome and functional dyspepsia. More detailed investigations aimed at neural processing in the brainstem, including the key relay station of the nucleus of the solitary tract (NTS), have hitherto been hampered by technical shortcomings. To ascertain these processes in more detail, we used multiecho multiband 7T functional magnetic resonance imaging and a novel translational experimental model based on a nutrient-derived intestinal chemonociceptive stimulus. In a randomized cross-over fashion, subjects received duodenal infusion of capsaicin (the pungent principle in red peppers) and placebo (saline). During infusion, functional magnetic resonance imaging data and concomitant symptom ratings were acquired. Of 26 healthy female volunteers included, 18 were included in the final analysis. Significantly increased brain activation over time during capsaicin infusion, as compared with placebo, was observed in brain regions implicated in pain processing, in particular the NTS. Brain activation in the thalamus, cingulate cortex, and insula was more pronounced in subjects who reported abdominal pain (visual analogue scale > 10 mm), as compared with subjects who experienced no pain. On the contrary, activations at the level of the NTS were independent of subjective pain ratings. The current experimental paradigm therefore allowed us to demonstrate activation of the principal relay station for visceral afferents in the brainstem, the NTS, which was engaged irrespective of the conscious pain response. These findings contribute to understanding the fundamental mechanism necessary for developing novel therapies aimed at correcting disturbances in visceral afferent pain processing.

Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study

Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences.

Reduced heart rate variability is related to the number of metabolic syndrome components and manifest diabetes in the sixth Tromsø study 2007-2008

Both diabetes mellitus (DM) and the metabolic syndrome (MetS) are associated with autonomic neuropathy, which predisposes to cardiac events and death. Measures of heart rate variability (HRV) can be used to monitor the activity of the autonomic nervous system (ANS), and there are strong indications that HRV can be used to study the progression of ANS-related diabetes complications. This study aims to investigate differences in HRV in healthy, MetS and diabetic populations. Based on 7880 participants from the sixth health survey in Tromsø (Tromsø 6, 2007–2008), we found a significant negative association between the number of MetS components and HRV as estimated from short-term pulse wave signals (PRV). This decrease in PRV did not appear to be linear, instead it leveled off after the third component, with no significant difference in PRV between the MetS and DM populations. There was a significant negative association between HbA1c and PRV, showing a decrease in PRV occurring already within the normal HbA1c range. The MetS and DM populations are different from healthy controls with respect to PRV, indicating impaired ANS in both conditions. In the future, a study with assessment of PRV measurements in relation to prospective cardiovascular events seems justified.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

Comparison of Heart Rate Variability Responses to Varying Resistance Exercise Volume-Loads

Purpose: The aim of this study was to compare the effects of low ([LV]; 4 total sets), moderate ([MV]; 8 total sets), and high set volumes ([HV]; 12 total sets) in acute full-body resistance exercise sessions on post-exercise parasympathetic reactivation measured using RMSSD. Methods: Ten resistance-trained participants (25.8 ± 6.8 yr., 173.4 ± 10.6 cm, 75.4 ± 9.9 kg) performed three resistance exercise sessions. During each session, heart rate variability (HRV) was measured pre- and for 30 min post-exercise, divided into 5-min segments stabilization, Post_5-10, Post_10-15, Post_15-20, Post_20-25, and Post_25-30. Repeated-measures ANOVA was used to assess differences within and between pre-post exercise natural logarithm RMSSD (LnRMSSD) values. To assess the initial change in LnRMSSD, the delta percent change (ΔLnRMSSD) from pre-exercise to Post_5-10 (ΔLnRMSSD_pre-post) was calculated for each session. The ΔLnRMSSD was also calculated between Post_5-10 and Post_25-30 (ΔLnRMSSD_post5-30) to assess recovery. Results: Significant differences were observed between sessions and when comparing pre-exercise values to all post-exercise times across sessions (p ≤ .05). The LV session resulted in significantly higher mean LnRMSSD value (3.62) post-exercise compared to both the MV (3.11, effect size [ES] =  3.77) and HV (3.02, ES =  3.92) sessions while the MV and HV sessions produced similar responses. Across sessions no return to baseline occurred and when comparing sessions, no significant differences were found in ΔLnRMSSD_pre-post or ΔLnRMSSD_post5-30. Conclusion: Acute bouts of full-body resistance exercise can cause similar reductions in LnRMSSD from pre-exercise levels and can delay parasympathetic reactivation back to baseline values during the same 30-min recovery period despite differences in set volume.

Heart-brain synchronization breakdown in Parkinson's disease

Heart rate variability (HRV) abnormalities are potential early biomarkers in Parkinson’s disease (PD) but their relationship with central autonomic network (CAN) activity is not fully understood. We analyzed the synchronization between HRV and brain activity in 31 PD patients and 21 age-matched healthy controls using blood-oxygen-level-dependent (BOLD) signals from resting-state functional brain MRI and HRV metrics from finger plethysmography recorded for 7.40 min. We additionally quantified autonomic symptoms (SCOPA-AUT) and objective autonomic cardiovascular parameters (blood pressure and heart rate) during deep breathing, Valsalva, and head-up tilt, which were used to classify the clinical severity of dysautonomia. We evaluated HRV and BOLD signals synchronization (HRV-BOLD-sync) with Pearson lagged cross-correlations and Fisher’s statistics for combining window-length-dependent HRV-BOLD-Sync Maps and assessed their association with clinical dysautonomia. HRV-BOLD-sync was lower significantly in PD than in controls in various brain regions within CAN or in networks involved in autonomic modulation. Moreover, heart-brain synchronization index (HBSI), which quantifies heart-brain synchronization at a single-subject level, showed an inverse exposure–response relationship with dysautonomia severity, finding the lowest HBSI in patients with severe dysautonomia, followed by moderate, mild, and, lastly, controls. Importantly, HBSI was associated in PD, but not in controls, with Valsalva pressure recovery time (sympathetic), deep breathing E/I ratio (cardiovagal), and SCOPA-AUT. Our findings support the existence of heart-brain de-synchronization in PD with an impact on clinically relevant autonomic outcomes.

Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography

OBJECTIVE

Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content.

METHODS

PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests.

RESULTS

From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz.

CONCLUSION

The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application.

SIGNIFICANCE

The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.

Examining weekly heart rate variability changes: a comparison between wearable devices running head: weekly heart rate variability changes

Monitoring heart rate variability has been commonly performed by different devices which differ in their methods (i.e., night recording vs. upon awakening measure, pulse vs. R waves, and software signal processing), Thus, the purpose of this study was to determine the level of agreement between different methods of heart rate variability monitoring, represented in two different systems (i.e., the Polar Nightly Recharge™ function present in Polar sport watches and the Polar H10 chest strap synchronized with the Kubios app). A group of 11 recreational athletes performed a concurrent training program for eight weeks and heart rate variability was daily monitored through both devices. Very large correlation (r = 0.714) and good reliability (ICC = 0.817) were obtained between devices through the entire training program. The magnitude-based inference method was also applied to determine the likelihood of the change concerning the smallest worthwhile change. From a baseline corresponding to the first two weeks of the training program, the weekly heart rate variability changes of the following six weeks were determined for each participant with each device. Despite the large correlation and good reliability between devices, there was a 60.6% of discordance in the likelihood interpretation of the change for the 66 weeks evaluated, explained by the random errors found. Thus, practitioners should be aware of these differences if their training groups use different devices or if an athlete interchanges them. The different nuances of each device can condition the heart rate variability data variation which could compromise the interpretation of the autonomic nervous system modulation.

Cardiovascular Autonomic Responses to Aerobic, Resistance and Combined Exercises in Resistance Hypertensive Patients

Here, we report the acute effects of aerobic (AER), resistance (RES), and combined (COM) exercises on blood pressure, central blood pressure and augmentation index, hemodynamic parameters, and autonomic modulation of resistant (RH) and nonresistant hypertensive (NON-RH) subjects. Twenty participants (10 RH and 10 NON-RH) performed three exercise sessions (i.e., AER, RES, and COM) and a control session. Hemodynamic (Finometer®, Beatscope), office blood pressure (BP), and autonomic variables (accessed through spectral analysis of the pulse-to-pulse BP signal, in the time and frequency domain-Fast Fourrier Transform) were assessed before (T0), one-hour (T1), and twenty-four (T2) hours after each experimental session. There were no changes in office BP, pulse wave behavior, and hemodynamic parameters after (T0 and T1) exercise sessions. However, AER and COM exercises significantly reduced sympathetic modulation in RH patients. It is worth mentioning that more significant changes in sympathetic modulation were observed after AER as compared to COM exercise. These findings suggest that office blood pressure, arterial stiffness, and hemodynamic parameters returned to baseline levels in the first hour and remained stable in the 24 hours after the all-exercise sessions. Notably, our findings bring new light to the effects of exercise on RH, indicating that RH patients show different autonomic responses to exercise compared to NON-RH patients. This trial is registered with trial registration number NCT02987452.

Heart Rate Variability from Wearable Photoplethysmography Systems: Implications in Sleep Studies at High Altitude

The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R–R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis.

On the spatial phase distribution of cutaneous low-frequency perfusion oscillations

Distributed cutaneous tissue blood volume oscillations contain information on autonomic nervous system (ANS) regulation of cardiorespiratory activity as well as dominating thermoregulation. ANS associated with low-frequency oscillations can be quantified in terms of frequencies, amplitudes, and phase shifts. The relative order between these faculties may be disturbed by conditions colloquially termed ‘stress’. Photoplethysmography imaging, an optical non-invasive diagnostic technique provides information on cutaneous tissue perfusion in the temporal and spatial domains. Using the cold pressure test (CPT) in thirteen healthy volunteers as a well-studied experimental intervention, we present a method for evaluating phase shifts in low- and intermediate frequency bands in forehead cutaneous perfusion mapping. Phase shift changes were analysed in low- and intermediate frequency ranges from 0.05 Hz to 0.18 Hz. We observed that time waveforms increasingly desynchronised in various areas of the scanned area throughout measurements. An increase of IM band phase desynchronization observed throughout measurements was comparable in experimental and control group, suggesting a time effect possibly due to overshooting the optimal relaxation duration. CPT triggered an increase in the number of points phase-shifted to the reference that was specific to the low frequency range for phase-shift thresholds defined as π/4, 3π/8, and π/2 rad, respectively. Phase shifts in forehead blood oscillations may infer changes of vascular tone due to activity of various neural systems. We present an innovative method for the phase shift analysis of cutaneous tissue perfusion that appears promising to assess ANS change processes related to physical or psychological stress. More comprehensive studies are needed to further investigate the reliability and physiological significance of findings.

The effect of using activity workstations on heart rate variability during complex cognitive tasks

ObjectivesThe purpose of the current study was to examine the effects of using an activity workstation on the physiological stress response as measured by heart rate variability while completing cognitively demanding tasks. Participants: Eleven college students (6 females; age: 19.4 ± 0.9 years) participated in the study. Methods: The participants completed three psychologically stressful cognitive tasks while seated at a traditional desk and while using an activity workstation. Heart rate variability was recorded and analyzed with power spectrum density and time-domain analysis. Results: Using activity workstations while completing stressful cognitive tasks did not negatively affect task performance. There was; however, a reduction in low frequency heart rate variability but no change in cardiac sympathovagal balance. Conclusion: The results indicate that using activity workstations while completing difficult tasks reduces sympathetic reactivity to stress in college students. This suggests that using activity workstations could provide a coping mechanism for stress.

Deep convolutional neural network-based signal quality assessment for photoplethysmogram

Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was to develop a signal quality assessment technology for photoplethysmogram (PPG) widely used in wearable healthcare. In this study, we developed and verified a deep neural network (DNN)-based signal quality assessment model using about 1.6 million 5-s segment length PPG big data of about 29 GB from the MIMIC III PPG waveform database. The DNN model was implemented through a 1D convolutional neural network (CNN). The number of CNN layers, number of fully connected nodes, dropout rate, batch size, and learning rate of the model were optimized through Bayesian optimization. As a result, 6 CNN layers, 1,546 fully connected layer nodes, 825 batch size, 0.2 dropout rate, and 0.002 learning rate were needed for an optimal model. Performance metrics of the result of classifying waveform quality into 'Good' and 'Bad', the accuracy, specificity, sensitivity, area under the receiver operating curve, and area under the precision-recall curve were 0.978, 0.948, 0.993, 0.985, 0.980, and 0.969, respectively. Additionally, in the case of simulated real-time application, it was confirmed that the proposed signal quality score tracked the decrease in pulse quality well.

Acute and Short-Term Autonomic and Hemodynamic Responses to Transcranial Direct Current Stimulation in Patients With Resistant Hypertension

Previously, we demonstrated that acute transcranial direct current stimulation (tDCS) reduced blood pressure (BP) and improved autonomic modulation in hypertensives. We hypothesized that acute and short-term tDCS intervention can promote similar benefits in resistant hypertensive patients (RHT). We assessed the impact of one (acute intervention) and ten (short-term intervention) tDCS or SHAM (20 min, each) sessions on BP, pulse interval (PI) and systolic blood pressure variabilities, humoral mechanisms associated with BP regulation, and cytokines levels. True RHT subjects (n = 13) were randomly submitted to one and ten SHAM and tDCS crossing sessions (1 week of “washout”). Hemodynamic (Finometer^®, Beatscope), office BP, and autonomic variables (accessed through spectral analysis of the pulse-to-pulse BP signal, in the time and frequency domain – Fast Fourrier Transform) were measured at baseline and after the short-term intervention. 24 h-ambulatory BP monitoring was measured after acute and short-term protocols. Acute intervention: tDCS reduced BP, cardiac output, and increase high-frequency band of PI (vagal modulation to the heart). Short-term protocol: tDCS did not change BP and cardiac output parameters. In contrast, central systolic BP (−12%), augmentation index (−31%), and pulse wave velocity (34%) were decreased by the short-term tDCS when compared to SHAM. These positive results were accompanied by a reduction in the low-frequency band (−37%) and an increase of the high-frequency band of PI (+62%) compared to SHAM. These findings collectively indicate that short-term tDCS concomitantly improves resting cardiac autonomic control and pulse wave behavior and reduces central BP in RHT patients, https://ensaiosclinicos.gov.br/rg/RBR-8n7c9p.

Validation of a smart shirt for heart rate variability measurements at rest and during exercise

Heart rate variability (HRV) monitoring is a promising option to estimate the autonomic nervous system regulation responding to exercise. Textiles with embedded sensors recording heartbeat intervals are a simple tool for data collection. So-called smart shirts offer comfort for a daily use and are managed easily. Their measurement accuracy for HRV calculation at rest is promising but remains questionable during exercise. Therefore, the present study validated the Ambiotex smart shirt using HRV indices (RMSSD, rel. HF power and rel. LF power) during exercise. Eighty-three healthy participants (31 ± 6 years; 39 females, 44 males) completed an incremental exercise test on a bicycle ergometer wearing the smart shirt and an electrocardiogram simultaneously. We compared HRV indices of segments at rest (5 min), at warm-up (3 min) and twice at the exercise test (each 5 min). At rest and at warm-up, we observed excellent linear relationship (r > 0.96; R² > 0.94), excellent relative reliability (ICC ≥ 0.98; α ≥ 0.98) and acceptable agreement (bias < 10%). During the exercise test, measurement accuracy declined with increasing intensity but remained high (> 0.8), although results for partial HRV indices were insufficient. In addition, percentage bias was unacceptable during exercise test. However, the findings support the validity of the smart shirt for measuring HRV especially at rest and at warm-up. We suggest using the smart shirt for monitoring HRV indices on a daily basis but caution should be taken in the interpretation of HRV indices obtained during moderate to vigorous exercise intensities. This article is protected by copyright. All rights reserved.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

The indirect impact of heart rate variability on cold pressor pain tolerance and intensity through psychological distress in individuals with chronic pain: the Tromsø Study

Supplemental Digital Content is Available in the Text. The hypoalgesic impact of cardiovascular regulatory systems on evoked pain responsiveness in those with chronic pain is conveyed via the indirect effects of psychological distress.

Introduction:

Chronic pain (CP) patients often display lower heart rate variability (HRV) and baroreceptor sensitivity (BRS), which are associated with increased evoked pain intensity and decreased pain tolerance.

Objective:

The purpose of this study was to test whether the association between low levels of HRV and BRS and increased evoked pain responsiveness in individuals with CP is mediated by psychological distress and whether this mediation is sex dependent.

Methods:

The sample consisted of 877 participants in Wave 6 of the Tromsø population study who reported clinically meaningful CP. Resting HRV and BRS parameters were derived from continuous beat-to-beat blood pressure recordings. Psychological distress was assessed using the Hopkins Symptom Checklist-10. After cardiovascular assessment, participants completed a 106-second cold pressor task (3°C bath), which assessed cold pressor pain intensity (CPI) and cold pressor pain tolerance (CPT).

Results:

In the full CP sample, mediation analyses showed significant indirect effects, without direct effects, of HRV and BRS on both CPT and CPI via psychological distress. When stratified by sex, significant indirect effects via psychological distress were only found in males for the impact of rMSSD on CPT, the impact of SDNN on CPT, and the impact of BRS on CPT via psychological distress. Moderated mediation analyses revealed that there were no significant sex differences in the indirect effects of HRV and BRS on both CPT and CPI via psychological distress.

Conclusions:

The hypoalgesic impact of cardiovascular regulatory systems on evoked pain responses is conveyed via the indirect effects of psychological distress.

Photoplethysmogram Analysis and Applications: An Integrative Review

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables' Data from the Crowd

Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20-46 and 24-46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject's data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects' data consumption.

Remote Healthcare for Elderly People Using Wearables: A Review

The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.

Heart rate and heart rate variability in patients with chronic inflammatory joint disease: the role of pain duration and the insular cortex

Background

Chronic inflammatory joint diseases (CIJD) have been linked to increased cardiovascular morbidity and mortality. A decisive reason could be a dysregulation of the autonomic nervous system, which is responsible for the control of cardiovascular function. So far, the cause of changes in autonomic nervous system functions remains elusive. In this study, we investigate the role of chronic pain and the insular cortex in autonomic control of cardiac functioning in patients with CIJD.

Methods

We studied the autonomic nervous system through the assessment of heart rate and heart rate variability (HRV) at rest and under cognitive stimulation. Furthermore, we investigated insular cortex volume by performing surface-based brain morphometry with FreeSurfer. For this study, 47 participants were recruited, 22 individual age- and sex-matched pairs for the magnetic resonance imaging analyses and 14 for the HRV analyses. All available patients’ data were used for analysis.

Results

Pain duration was negatively correlated with the resting heart rate in patients with chronic inflammatory joint diseases (n = 20). In a multiple linear regression model including only CIJD patients with heart rate at rest as a dependent variable, we found a significant positive relationship between heart rate at rest and the volume of the left insular cortex and a significant negative relationship between heart rate at rest and the volume of the right insular cortex. However, we found no significant differences in HRV parameters or insular cortex volumes between both groups.

Conclusions

In this study we provide evidence to suggest insular cortex involvement in the process of ANS changes due to chronic pain in CIJD patients.

The study was preregistered with the German Clinical Trials Register (https://www.drks.de; DRKS00012791; date of registration: 28 July 2017).

Pulse Oximetry: The Working Principle, Signal Formation, and Applications

Pulse oximeters are routinely used in various medical-grade and consumer-grade applications. They can be used to estimate, for example, blood oxygen saturation, autonomic nervous system activity and cardiac function, blood pressure, sleep quality, and recovery through the recording of photoplethysmography signal. Medical-grade devices often record red and infra-red light-based photoplethysmography signals while smartwatches and other consumer-grade devices usually rely on a green light. At its simplest, a pulse oximeter can consist of one or two photodiodes and a photodetector attached, for example, a fingertip or earlobe. These sensors are used to record light absorption in a medium as a function of time. This time-varying absorption information is used to form a photoplethysmography signal. In this chapter, we discuss the working principles of pulse oximeters and the formation of the photoplethysmography signal. We will further discuss the advantages and disadvantages of pulse oximeters, which kind of applications exist in the medical field, and how pulse oximeters are utilized in daily health monitoring.

Long- and short-term fluctuations compared for several organ systems across sleep stages

Some details of cardiovascular and cardio-respiratory regulation and their changes during different sleep stages remain still unknown. In this paper we compared the fluctuations of heart rate, pulse rate, respiration frequency, and pulse transit times as well as EEG alpha-band power on time scales from 6 to 200 s during different sleep stages in order to better understand regulatory pathways. The five considered time series were derived from ECG, photoplethysmogram, nasal air flow, and central electrode EEG measurements from full-night polysomnography recordings of 246 subjects with suspected sleep disorders. We applied detrended fluctuation analysis, distinguishing between short-term (6-16 s) and long-term (50-200 s) correlations, i.e., scaling behavior characterized by the fluctuation exponents α ₁ and α ₂ related with parasympathetic and sympathetic control, respectively. While heart rate (and pulse rate) are characterized by sex and age-dependent short-term correlations, their long-term correlations exhibit the well-known sleep stage dependence: weak long-term correlations during non-REM sleep and pronounced long-term correlations during REM sleep and wakefulness. In contrast, pulse transit times, which are believed to be mainly affected by blood pressure and arterial stiffness, do not show differences between short-term and long-term exponents. This is in constrast to previous results for blood pressure time series, where α ₁ was much larger than α ₂, and therefore questions a very close relation between pulse transit times and blood pressure values. Nevertheless, very similar sleep-stage dependent differences are observed for the long-term fluctuation exponent α ₂ in all considered signals including EEG alpha-band power. In conclusion, we found that the observed fluctuation exponents are very robust and hardly modified by body mass index, alcohol consumption, smoking, or sleep disorders. The long-term fluctuations of all observed systems seem to be modulated by patterns following sleep stages generated in the brain and thus regulated in a similar manner, while short-term regulations differ between the organ systems. Deviations from the reported dependence in any of the signals should be indicative of problems in the function of the particular organ system or its control mechanisms.

Validity of the Wrist-Worn Polar Vantage V2 to Measure Heart Rate and Heart Rate Variability at Rest

Heart rate (HR) and heart rate variability (HRV) can be monitored with wearable devices throughout the day. Resting HRV in particular, reflecting cardiac parasympathetic activity, has been proposed to be a useful marker in the monitoring of health and recovery from training. This study examined the validity of the wrist-based photoplethysmography (PPG) method to measure HR and HRV at rest. Recreationally endurance-trained participants recorded pulse-to-pulse (PP) and RR intervals simultaneously with a PPG-based watch and reference heart rate sensor (HRS) at a laboratory in a supine position (n = 39; 5-min recording) and at home during sleep (n = 29; 4-h recording). In addition, analyses were performed from pooled laboratory data (n = 11344 PP and RR intervals). Differences and correlations were analyzed between the HRS- and PPG-derived HR and LnRMSSD (the natural logarithm of the root mean square of successive differences). A very good agreement was found between pooled PP and RR intervals with a mean bias of 0.17 ms and a correlation coefficient of 0.993 (p < 0.001). In the laboratory, HR did not differ between the devices (mean bias 0.0 bpm), but PPG slightly underestimated the nocturnal recordings (mean bias -0.7 bpm, p < 0.001). PPG overestimated LnRMSSD both in the laboratory (mean bias 0.20 ms, p < 0.001) and nocturnal recordings (mean bias 0.17 ms, p < 0.001). However, very strong intraclass correlations in the nocturnal recordings were found between the devices (HR: 0.998, p < 0.001; LnRMSSD: 0.931, p < 0.001). In conclusion, PPG was able to measure HR and HRV with adequate accuracy in recreational athletes. However, when strict absolute values are of importance, systematic overestimation, which seemed to especially concern participants with low LnRMSSD, should be acknowledged.

Relationship Between Heart Rate Variability and Pulse Rate Variability Measures in Patients After Coronary Artery Bypass Graft Surgery

Background: Heart rate variability (HRV) and pulse rate variability (PRV) measures are two kinds of physiological indices that can be used to evaluate the autonomic nervous function of healthy subjects and patients with various kinds of illness.

Purpose: In this study, we compared the agreement and linear relationship between electrocardiographic signals (ECG)-derived HRV and photoplethysmographic signals (PPG)-derived right hand PRV (R-PRV) and left hand PRV (L-PRV) measures in 14 patients over 1 year after coronary artery bypass graft (CABG) surgery.

Method: The ECG and PPG signals of the patient were recorded simultaneously for 10 min in a supine position. The last 512 stationary RR intervals (RRI) and peak-to peak intervals (PPI) of pulse wave were derived for data analysis. Bland-Altman plot was used to assess the agreement among HRV and both hand PRV measures, while linear regression analysis was used to examine the relationship among corresponding measures of HRV, R-PRV, and L-PRV.

Result: The results revealed significant differences in total power (TP), very low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), and normalized VLF (VLFnorm) among HRV, R-PRV, and L-PRV. Bland-Altman plot analysis showed good agreements in almost all measures between R-PRV and L-PRV, except insufficient agreement was found in LF/HF. Insufficient agreements were found in root mean square successive difference (RMSSD), normalized HF (HFnorm), and LF/HF indices between HRV and L-PRV, and in VLFnorm, HFnorm, and LF/HF indices between HRV and R-PRV. Linear regression analysis showed that the HRV, R-PRV, and L-PRV measures were all highly correlated with one another (r = 0.94 ~ 1; p < 0.001).

Conclusion: Though PRV measures of either hand are not surrogates of HRV measures, they might still be used to evaluate the autonomic nervous functions of CABG patients due to the moderate to good agreements in most time-domain and frequency-domain HRV measures and the strong and positive correlations among HRV and both hands PRV measures in CABG patients.

ACUTE TRANSCRANIAL DIRECT CURRENT STIMULATION (tDCS) IMPROVES VENTILATORY VARIABILITY AND AUTONOMIC MODULATION IN RESISTANT HYPERTENSIVE PATIENTS

Here, we assessed the impact of one session of transcranial direct current stimulation (tDCS) or SHAM (20 min, each) on ventilatory responses to cardiopulmonary exercise test, central and peripheral blood pressure (BP), and autonomic modulation in resistant hypertensive (RHT) patients. RHT subjects (n = 13) were randomly submitted to SHAM and tDCS crossing sessions (1 week of "washout"). Patients and a technician who set the tDCS/Sham room up were both blind. After brain stimulation, patients were submitted to a cardiopulmonary exercise test to evaluate ventilatory and cardiovascular response to exercise. Hemodynamic (Finometer®, Beatscope), and autonomic variables were measured at baseline (before tDCS/Sham) and after incremental exercise. RESULTS: Our study shows that tDCS condition improved heart rate recovery, VO2 peak, and vagal modulation (after cardiopulmonary exercise test); attenuated the ventilatory variability response, central and peripheral blood pressure well as sympathetic modulation (after cardiopulmonary exercise test) in comparison with SHAM. These data suggest that acute tDCS sessions prevented oscillatory ventilation behavior during the cardiopulmonary exercise test and mitigated the increase of systolic blood pressure in RHT patients. After the exercise test, tDCS promotes better vagal reentry and improved autonomic modulation, possibly reducing central blood pressure and aortic augmentation index compared to SHAM. Brazilian Registry of Clinical Trials (ReBEC): https://ensaiosclinicos.gov.br/rg/RBR-8n7c9p.

Digital Health and Care Study on Elderly Monitoring

Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.

Impact of Equine-Assisted Interventions on Heart Rate Variability in Two Participants with 22q11.2 Deletion Syndrome: A Pilot Study

People with disabilities due to genetic origin often present high levels of stress: non-pharmacological interventions such as Equine-Assisted Interventions (EAI) may be a useful strategy. The objective of this pilot study was to evaluate stress levels in two participants with 22q11.2 deletion syndrome diagnosis, immediately after carrying out the EAI. A single case experimental design methodology was chosen due to the small sample size. Two participants with 22q11.2 Deletion Syndrome, a rare disease, with different comorbidities were included. The present study considered the EAI as the independent variable while the Heart Rate Variability (HRV) represented the dependent one, as HRV is considered an indicator of stress level. Measurements were performed before and after carrying out the interventions. The results showed an HRV increase in one of the participants and an increase in the arousal level evidenced by a decrease in his HRV. After having carried out the program, EAI seems to cause an impact on the activation level of the participants depending on the typology and nature of the intervention. However, these results should be treated with caution due to the small sample size. This study is a pilot to test the feasibility of the proposed interventions on the variable under study.

Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment

Heart rate variability (HRV) is regularly assessed in neuroimaging studies as an indicator of autonomic, emotional or cognitive processes. In this study, we investigated the influence of a loud and cramped environment during magnetic resonance imaging (MRI) on resting HRV measures. We compared recordings during functional MRI sessions with recordings in our autonomic laboratory (LAB) in 101 healthy subjects. In the LAB, we recorded an electrocardiogram (ECG) and a photoplethysmogram (PPG) over 15 min. During resting state functional MRI, we acquired a PPG for 15 min. We assessed anxiety levels before the scanning in each subject. In 27 participants, we performed follow-up sessions to investigate a possible effect of habituation. We found a high intra-class correlation ranging between 0.775 and 0.996, indicating high consistency across conditions. We observed no systematic influence of the MRI environment on any HRV index when PPG signals were analyzed. However, SDNN and RMSSD were significantly higher when extracted from the PPG compared to the ECG. Although we found a significant correlation of anxiety and the decrease in HRV from LAB to MRI, a familiarization session did not change the HRV outcome. Our results suggest that psychological factors are less influential on the HRV outcome during MRI than the methodological choice of the cardiac signal to analyze.

Don't stress, it's under control: Neural correlates of stressor controllability in humans

Animal research has repeatedly shown that control is a key variable in the brain's stress response. Uncontrollable stress triggers a release of monoamines, impairing prefrontal functions while enhancing subcortical circuits. Conversely, control over an adverse event involves prefrontally mediated downregulation of monoamine nuclei and is considered protective. However, it remains unclear to what extent these findings translate to humans. During functional magnetic resonance imaging, we subjected participants to controllable and uncontrollable aversive but non-painful electric stimuli, as well as to a control condition without aversive stimulation. In each trial, a symbol signalled whether participants could terminate the stressor through correct performance in a button-matching task or whether the stressor would be randomly terminated, i.e., uncontrollable. Along with neural responses, we assessed participants' accuracy, reaction times, and heart rate. To relate neural activations and subjective experience, we asked participants to rate perceived control, helplessness, and stress. Results were largely in line with our hypotheses. The vmPFC was generally deactivated by aversive stimulation, but this effect was attenuated when participants could terminate the stressor compared to when their responses had no effect. Furthermore, activation in stress-responsive regions, including the bilateral insula, was reduced during controllable trials. Under uncontrollable aversive stimulation, greater vmPFC recruitment was linked to reduced feelings of helplessness. An investigation of condition-dependant differences in vmPFC connectivity yielded no significant results. Our findings further corroborate animal research and emphasise the role of the vmPFC in controllability-dependant regulation of stress responses. Based on the results, we discuss future directions in the context of resilience research and mental health promotion.

Reliability of Pulse Rate Variability in Elderly Men and Women: an Application of Cross-Mapping Approach

Photoplethysmography (PPG) is a completely noninvasive, optical method of assessing blood flow dynamics in peripheral vasculature. Wearable devices for PPG recording are becoming increasingly popular, due to their cost-effectiveness and ease of use. For these reasons, many recent scientific studies have proposed the use of pulse rate variability (PRV) extracted from PPG as a surrogate for heart rate variability (HRV), in monitoring autonomic activity and cardiovascular health.In this work, we used a cross-mapping approach, a methodology based on chaos theory, to compare PRV and HRV dynamics, and investigate their agreement according to age and gender of healthy subjects. We used ECG and PPG data acquired from 57 subjects (41 young and 16 elderly) during resting state in the supine position. Signals were gathered from the publicly available VORTAL dataset. Our results showed a statistically significant decrease of PRV reliability as an HRV surrogate in old participants, which was confirmed as significant when only men subjects were analyzed (p-value<0.01).Our findings, although preliminary, suggest greater caution in the use of PPG devices for monitoring cardiovascular health, especially in elderly men.

RMSSD Estimation From Photoplethysmography and Accelerometer Signals Using a Deep Convolutional Network

Heart Rate Variability is a significant indicator of the Autonomic Neural System's functioning, traditionally evaluated from electrocardiogram recordings. Photoplethysmography sensors, like electrocardiograph devices, track the heart's activity and have been widely popularized by their use in smart watches and fitness trackers. In this study we develop a deep learning based approach which is able to successfully estimate the patient's Root Mean Square of the Successive Differences, a common heart rate variability metric, from lower quality, less expensive photoplethysmography sensors under a wide range of conditions.

Effect of Filtering of Photoplethysmography Signals in Pulse Rate Variability Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:5500-5503. [PMID: 34892370 DOI: 10.1109/embc46164.2021.9629521]

Due to the widespread use and simplicity of photoplethysmography (PPG) signals, and because this signal contains information related to pulse rate, several studies have started to propose the use of Pulse Rate Variability (PRV) for the assessment of cardiovascular autonomic nervous activity, instead of using Heart Rate Variability (HRV) obtained with the electrocardiogram (ECG). However, there is a lack of standardisation and guidelines for the measurement of PRV from PPG signals, which might hinder comparability among studies and validation of results. The aim of this study was to evaluate different digital filters on PPG signals and their effects on PRV information, compared to HRV obtained from ECG. PPG and ECG signals obtained from healthy volunteers were used to measure HRV and PRV. PPG signals were filtered using different FIR and IIR digital filters, with several cut-off frequencies. The results indicate that filtering PPG signals using IIR filters and lower low-cut-off frequencies allow for the acquisition of more reliable PRV information, with lower Bland-Altman ratios and higher cross-correlations when compared to HRV. This is a first step in establishing guidelines and standards for the analysis of PRV information using PPG signals.Clinical relevance- Pulse rate variability might be a useful tool for the assessment of the cardiovascular autonomic nervous system. This study is the first step for establishing standards of measurement of this signal, which helps in the comparability and validation of the technique.

Tracking transient changes in the intrinsic neural frequency architecture: Oxytocin facilitates non-harmonic relationships between alpha and theta rhythms in the resting brain

Shifts in the peak frequencies of oscillatory neural rhythms have been put forward as a principal mechanism by which cross-frequency coupling and decoupling is implemented in the brain. This notion is based on the mathematical reality that neural oscillations can only fully synchronize when their peak frequencies form harmonic 2:1 relationships (e.g., f₂=f₁/2). Non-harmonic cross-frequency relationships, on the other hand (based on the irrational golden mean 1.618.:1), provide the highest physiologically possible desynchronized state (reducing the occurrence of spurious, noisy, background coupling), and are therefore anticipated to characterize the resting state of the brain, in which no selective information processing takes place. The present study sought to assess whether the transient occurrence of 1.6:1 non-harmonic and 2:1 harmonic relationships between peak frequencies in the alpha (8-14 Hz) and theta (4-8 Hz) bands - respectively facilitating states of decoupling or coupling between oscillatory rhythms - are impacted by the intranasal administration of a single-dose of oxytocin (OT) or placebo. To do so, continuous resting-state electroencephalography (5 min eyes open, 19 electrodes) was obtained from 96 healthy adult men before and after nasal spray administration. The transient formation of non-harmonic cross-frequency configurations between alpha and theta peak frequencies was significantly increased after OT nasal spray administration, indicating an effect of OT on reducing the intrinsic occurrence of spurious (noisy) background phase synchronizations during resting-state. As a group, the OT group also showed a significant parallel increase in high-frequency and decrease in low-frequency heart rate variability, confirming a homeostatic role of OT in balancing parasympathetic drive. Overall, non-harmonic cross-frequency configurations have been put forward to lay the ground for a healthy neural network allowing the opportunity for an efficient transition from resting state to activity. The observed effects of OT on cross-frequency dynamics are therefore interpreted to reflect a homeostatic role of OT in increasing the signal-to-noise properties of the intrinsic EEG neural frequency architecture, i.e., by precluding the occurrence of 'noisy', unwanted, spurious couplings among neural rhythms in the resting brain.

Reducing Motion Artifacts of Pulse Intervals from Photoplethysmogram of a Commercial Wristband for Heart Rate Variability Analysis

Heart rate monitoring based on photoplethysmography (PPG) is a noninvasive and inexpensive way of measuring many important cardiovascular metrics such as heart rate and heart rate variability, and has been used in many wearable devices. Unfortunately, the accuracy of the measurements is compromised by motion artifacts. We propose a theoretically sound method to reduce the motion artifacts of heart rate sensed by a commercial wristband. This method is based on outlier detection and singular spectrum analysis which enables us to reduce the movement-related noise in non-stationary signals. The results suggest that this method exhibits high correspondence to the simultaneously measured heart rate using ECG. Several metrics of heart rate variability computed from cleaned data also indicate high agreement with those obtained from ECG.