Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses

Combined Proxies for Heart Rate Variability as a Global Tool to Assess and Monitor Autonomic Dysregulation in Fibromyalgia and Disease-Related Impairments

BACKGROUND

Heart rate variability (HRV) provides both linear and nonlinear autonomic proxies that can be informative of health status in fibromyalgia (FM), where sympatho-vagal abnormalities are common. This retrospective observational study aims to: 1. detect differences in correlation dimension (D2) between FM patients and healthy controls (HCs); 2. correlate D2 with standard HRV parameters; 3. correlate the degree of HRV changes using a global composite parameter called HRV grade, derived from three linear indices (SDNN = intervals between normal sinus beats; RMSSD = mean square of successive differences; total power), with FM clinical outcomes; 4. correlate all linear and nonlinear HRV parameters with clinical variables in patients.

METHODS

N = 85 patients were considered for the analysis and compared to 35 healthy subjects. According to standard diagnostic protocol, they underwent a systematic HRV protocol with a 5-min paced breathing task. Disease duration, pain intensity, mood, sleep, fatigue, and quality of life were assessed. Non-parametric tests for independent samples and pairwise correlations were performed using JMP (all p < 0.001).

RESULTS

Mann-Whitney U found a significant difference in D2 values between FM patients and HCs (p < 0.001). In patients, D2 was associated with all HRV standard indices (all p < 0.001) and FM impairment (FIQ = -0.4567; p < 0.001). HRV grade was also associated with FM impairment (FIQ = 0.5058; p < 0.001).

CONCLUSION

Combining different HRV measurements may help understand the correlates of autonomic dysregulation in FM. Specifically, clinical protocols could benefit from the inclusion and validation of D2 and HRV parameters to target FM severity and related dysautonomia.

Heart rate variability with circadian rhythm removed achieved high accuracy for stress assessment across all times throughout the day

Background

Assessing real-time stress in individuals to prevent the accumulation of stress is necessary due to the adverse effects of excessive psychological stress on health. Since both stress and circadian rhythms affect the excitability of the nervous system, the influence of circadian rhythms needs to be considered during stress assessment. Most studies train classifiers using physiological data collected during fixed short time periods, overlooking the assessment of stress levels at other times.

Methods

In this work, we propose a method for training a classifier capable of identifying stress and resting states throughout the day, based on 10 short-term heart rate variability (HRV) feature data obtained from morning, noon, and evening. To characterize the circadian rhythms of HRV features, heartbeat interval data were collected and analyzed from 50 volunteers over three consecutive days. The circadian rhythm trends in the HRV features were then removed using the Smoothness Priors Approach (SPA), and XGBoost models were trained to assess stress.

Results

The results show that all HRV features exhibit 12-h and 24-h circadian rhythms, and the circadian rhythm differences across different days for individuals are relatively small. Furthermore, training classifiers on detrended data can improve the overall accuracy of stress assessment across all time periods. Specifically, when combining data from different time periods as the training dataset, the accuracy of the classifier trained on detrended data increases by 13.67%.

Discussion

These findings indicate that using HRV features with circadian rhythm trends removed is an effective method for assessing stress at all times throughout the day.

Wearing WHOOP More Frequently Is Associated with Better Biometrics and Healthier Sleep and Activity Patterns

Wearable devices are increasingly used for health monitoring, yet the impact of consistent wear on physiological and behavioral outcomes is unclear. Leveraging nearly a million days and nights of longitudinal data from 11,914 subscribers, we examined the associations between the frequency of wearing a wrist-worn wearable device (WHOOP Inc., Boston, MA, USA) and 12-week changes in biometric, sleep, and activity profiles, modeling both between- and within-person effects. Higher average wear frequency and week-to-week increases in wear were associated with a lower resting heart rate (RHR), higher heart rate variability (HRV), longer and more consistent sleep, and greater weekly and daily physical activity duration (Ps < 0.01). A within-person multiple mediation analysis indicated that increased sleep duration partially mediated the association between wear frequency and a standardized (z-scored) RHR (indirect effect = -0.0387 [95% CI: -0.0464, -0.0326]), whereas physical activity minutes did not (indirect effect = 0.0003 [95% CI: -0.0036, 0.0040]). A Granger causality analysis revealed a modest but notable association between prior wear frequency and future RHR in participants averaging ≤5 days of weekly wear (p < 0.05 in 10.92% of tests). While further research is needed, our findings provide real-world evidence that sustained wearable engagement may support healthier habits and improved physiological outcomes over time.

Validity of Heart Rate Variability Measured with Apple Watch Series 6 Compared to Laboratory Measures

We assessed the test validity of the Apple Watch series 6 measure of heart rate variability (HRV) by comparing it with the reference measure assessed via a Biopac 3-lead electrocardiogram (ECG). We recruited 78 healthy adults (aged 20-75 years). HRV was measured using an in-lab protocol while resting, talking, watching a movie, before walking, and after walking. We conducted a synchronized countdown for each condition to guarantee that the recordings would be aligned between the two devices by using event markers in the Biopac at the exact time that the Apple Watch Breathe app began and ended. We assessed test validity using the Bland-Altman method, and both precision and accuracy were estimated using Lin's concordance correlation coefficient. The highest level of agreement and concordance between devices occurred during rest. We observed near-perfect agreement for R-R intervals and beats per minute (BPM) measures, with mean absolute percentage errors (MAPE) of 1.15% during resting conditions. We observed moderate levels of agreement and concordance for N-N intervals at rest with a MAPE of 31.31% during resting conditions. The Apple Watch provides a high level of validity for measuring R-R intervals and BPM in healthy adults. Further research is needed to determine if HRV measures with the Apple Watch offer a significant opportunity for the surveillance of CVD risk.

Noncontact evaluation of autonomic nervous system activity during exercise by using video analysis

Introduction

Autonomic nervous system activity (ANSA) plays a crucial role in the physical condition experienced during exercise and prolonged physical activity. In other words, ANSA is related to exercise performance and physical condition. Therefore, it is important to continuously monitor ANSA during high-intensity and sustained exercise. To this end, an uncomplicated and noncontact measurement system is preferable. Hence, in this study, we propose a method for the noncontact measurement of capillary contraction and dilation state, representative of ANSA, using a common commercial camera.

Methods

Specifically, we focused on alterations in the green value of facial video images, from which we derived the green-to-blue (G/B) ratio as an indicator of blood vessel dilation and contraction, and to facilitate assessment of their activity. We performed a validation experiment involving exercise tasks using an ergometer in 10 healthy adults (23 ± 1.6 years old). The G/B ratio shows the state of contraction and expansion of facial capillaries, and it was evaluated using heart rate as ground truth data of the fluctuation of autonomic nerve activity.

Results

We observed an increase in heart rate with decreased G/B ratio during exercise in all subjects. Postexercise, the heart rate decreased but the G/B ratio increased.

Discussion

During exercise, characterized by dominant sympathetic NSA, the G/B ratio decreased, and recovered after exercise when parasympathetic NSA was dominant. In this way, noncontact evaluation of ASNA was achieved by using the G/B ratio. In the future, this measurement system will be applied to functional tests for heat acclimation.

Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate

Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30 s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R2 scores of 0.6699 for 13 NNIs, 0.8751 for 26 NNIs, and 0.9358 for 39 NNIs, surpassing the time-segmented approach, which yielded 0.4162, 0.6528, and 0.7943 for 10, 20, and 30-s intervals, respectively. These findings demonstrate that using NNI counts for ultra-short-term stress prediction improves accuracy by accounting for individual bio-signal variations.

Validity of Ultra-Short-Term Heart Rate Variability Derived from Femoral Arterial Pulse Waveform in a British Military Cohort

Various non-electrocardiogram (ECG) based methods are considered reliable sources of heart rate variability (HRV) measurement. However, the ultra-short recording of a femoral arterial waveform has never been validated against the gold-standard ECG-based 300s HRV and was the aim of this study.A validity study was conducted using a sample from the first follow-up of the longitudinal ADVANCE study UK. The participants were adult servicemen (n = 100); similar in age, rank, and deployment period (Afghanistan 2003-2014). The femoral arterial waveforms (14s) from the pulse wave velocity (PWV) assessment, and ECG (300s) were recorded at rest in the supine position using the Vicorder™ and Bittium Faros™ devices, respectively, in the same session. HRV analysis was performed using Kubios Premium. Resting heart rate (HR) and root mean square of successive differences (RMSSD) were reported. The Bland-Altman %plots were constructed to explore the PWV-ECG agreement in HRV measurement. A further exploratory analysis was conducted across methods and durations.The participants' mean age was 38.0 ± 5.3 years. Both PWV-derived HR (r = 0.85) and RMSSD (rs=0.84) showed strong correlations with their 300s-ECG counterparts (p < 0.001). Mean HR was significantly higher with ECG than PWV (mean bias: -12.71 ± 7.73%, 95%CI: -14.25%, -11.18%). In contrast, the difference in RMSSD between the two methods was non-significant [mean bias: -2.90 ± 37.82% (95%CI: -10.40%, 4.60%)] indicating good agreement. An exploratory analysis of 14s ECG-vs-300s ECG measurement revealed strong agreement in both RMSSD and HR.The 14s PWV-derived RMSSD strongly agrees with the gold-standard (300s-ECG-based) RMSSD at rest. Conversely, HR appears method sensitive.

Effect of acupressure in the third eye point (EX-HN 3) on psychological distress, comfort and physiologic parameters among patients undergoing coronary angiography

AIM

The present study aimed to investigate the effect of acupressure on comfort, anxiety, stress, depression, and vital signs in patients undergoing coronary angiography.

METHODS

This randomized clinical trial was conducted on patients who underwent coronary angiography in Tehran, Iran. Seventy patients were randomly assigned to the intervention and control groups. The intervention protocol consisted of 20 min of acupressure applied to the Yintang point, and standard medical care was applied to the control group. Depression, Anxiety, and Stress questionnaire (DASS-21), General Comfort Questionnaire (GCQ) questionnaires, and standard monitoring were used as data collection tools before and after intervention, as well as after angiography. Data were analyzed using an independent sample t-test, chi-squared, and analysis of variance of repeated measures in SPSS software, and the level of significance was set at 0.05.

FINDINGS

The results showed that before acupressure, there was no statistically significant difference between the two groups. Anxiety and stress scores and comfort levels decreased significantly after the intervention (p < 0.001), while no significant difference was observed in the depression score (p = 0.873). There was a significant decrease in the blood pressure, breathing rate, and heart rate in the intervention group.

CONCLUSION

Acupressure can reduce the anxiety and stress of angiography candidates and make them more comfortable. It also reduces the blood pressure, breathing rate, and heart rate. Further studies at different pressure points and on a larger and more detailed scale are necessary.

Efficacy and Safety of Acupuncture for Restless Legs Syndrome in Patients with End-Stage Renal Disease: A Randomized-Controlled Trial at Hospital-Based Hemodialysis Center

Background: Restless legs syndrome (RLS) is frequent in patients with hemodialysis (HD) and occurs predominantly in its most severe forms. The study was conducted to evaluate the efficacy and safety of acupuncture for RLS in patients with end-stage renal disease (ESRD) at hospital-based HD center. Methods: This single-blind, randomized controlled trial was performed on patients with HD and RLS who were randomly assigned to the experimental group and control group. Data were collected using the International Restless Legs Syndrome Rating Scale (IRLSRS), Insomnia Severity Index (ISI), and heart rate variability (HRV) records at baseline, after the therapeutic course (12 times/4 weeks), and 1-week follow-up. Result: A total of 47 patients were evaluated with IRLSRS score from 11 to 30 in this study. There were 41 patients enrolled in the study based on inclusion/exclusion criteria and allocated randomly into two groups. A total of 35 participants completed the trial, including 18 subjects in the experimental group and 17 subjects in the control group. The comparison of IRLSRS and ISI showed a significant reduction between two groups after acupuncture treatment (p = 0.002, p = 0.003). The ISI after 1-week follow-up also revealed significant decrease (p = 0.003). This HRV results showed that high frequency (HF%) increased significantly (p = 0.021) and low frequency (LF%) decreased significantly in the acupuncture group (p = 0.021). The generalized estimating equation showed that the IRLSRS improved by 2.902 points (p < 0.001) in the acupuncture group compared with the control group and by 1.340 points (p = 0.003) after 1-week follow-up. There were no adverse effects observed during HD in this study. Discussion: The authors conclude that acupuncture could effectively improve the symptoms of RLS significantly. The results from this study provide clinical evidence on the efficacy and safety of acupuncture to treat the patients with RLS at the HD center.

Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study

BACKGROUND

Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information.

OBJECTIVE

In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19.

METHODS

A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies.

RESULTS

The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score.

CONCLUSIONS

We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

Agreement between Ultra-Short-Term and Standard Heart Rate Variability Analysis in Resting and Post-Exercise Conditions

BACKGROUND

Short-term (5 min) heart rate variability (HRV) analysis is widely used in assessing autonomic nervous system activity during exercise. While shortening the HRV measurement duration can help improve its application efficiency, its accuracy needs to be verified. This study investigated the agreement between ultra-short-term (UST) HRV (3 or 4 min) and standard 5 min HRV and explored the optimal recording duration under resting and post-exercise conditions.

METHODS

Fourteen participants exercised on a cycle ergometer at 60% of their maximum peak power. Data were collected during the rest condition (Pre-E) and three post-exercise conditions (Post-E1, Post-E2, and Post-E3), with indicators of the standard deviation (SDNN) of the ultra-short and short-term RR intervals and the root mean square (RMSSD) of the continuous difference between RR intervals. Repeated measures ANOVA, Cohen's d statistic, Bland-Altman analysis, and interclass correlation coefficients (ICC) assessed the agreement between UST-HRV and ST-HRV.

RESULTS

The consistency results of SDNN and RMSSD in resting and post-exercise were different. At the Pre-E, Post-E2, and Post-E3 phases, no statistical differences for SDNN and RMSSD were observed, with ICCs surpassing 0.9, indicating a high level of agreement. However, at Post-E2, there was a significant difference between 3 min RMSSD and 5 min RMSSD (p < 0.05), as well as between 3 min SDNN, 4 min SDNN, and 5 min SDNN (p < 0.05). Furthermore, the limits of agreement were observed to decrease as the time duration increased in Bland-Altman plots.

CONCLUSIONS

UST-HRV analysis is a reliable substitute for standard 5 min HRV assessment, particularly during resting conditions. For post-exercise measurements, assessing the appropriateness of a 3- or 4 min duration based on the exercise's length is recommended to ensure accuracy and reliability.

Impact of age-related gut microbiota dysbiosis and reduced short-chain fatty acids on the autonomic nervous system and atrial fibrillation in rats

Objective

Aging is the most significant contributor to the increasing prevalence of atrial fibrillation (AF). Dysbiosis of gut microbiota has been implicated in age-related diseases, but its role in AF development remains unclear. This study aimed to investigate the correlations between changes in the autonomic nervous system, short-chain fatty acids (SCFAs), and alterations in gut microbiota in aged rats with AF.

Methods

Electrophysiological experiments were conducted to assess AF induction rates and heart rate variability in rats. 16S rRNA gene sequences extracted from fecal samples were used to assess the gut microbial composition. Gas and liquid chromatography-mass spectroscopy was used to identify SCFAs in fecal samples.

Results

The study found that aged rats exhibited a higher incidence of AF and reduced heart rate variability compared to young rats. Omics research revealed disrupted gut microbiota in aged rats, specifically a decreased Firmicutes to Bacteroidetes ratio. Additionally, fecal SCFA levels were significantly lower in aged rats. Importantly, correlation analysis indicated a significant association between decreased SCFAs and declining heart rate variability in aged rats.

Conclusions

These findings suggest that SCFAs, as metabolites of gut microbiota, may play a regulatory role in autonomic nervous function and potentially influence the onset and progression of AF in aged rats. These results provide novel insights into the involvement of SCFAs and autonomic nervous system function in the pathogenesis of AF. These results provide novel insights into the involvement of SCFAs and autonomic nervous system function in the pathogenesis of AF.

Cardiovascular Autonomic Dysfunction in Hospitalized Patients with a Bacterial Infection: A Longitudinal Observational Pilot Study in the UK

PURPOSE

A temporal reduction in the cardiovascular autonomic responses predisposes patients to cardiovascular instability after a viral infection and therefore increases the risk of associated complications. These findings have not been replicated in a bacterial infection. This pilot study will explore the prevalence of cardiovascular autonomic dysfunction (CAD) in hospitalized patients with a bacterial infection.

METHODS

A longitudinal observational pilot study was conducted. Fifty participants were included: 13 and 37 participants in the infection group and healthy group, respectively. Recruitment and data collection were carried out during a two-year period. Participants were followed up for 6 weeks: all participants' cardiovascular function was assessed at baseline (week 1) and reassessed subsequently at week 6 so that the progression of the autonomic function could be evaluated over that period of time. The collected data were thereafter analyzed using STATA/SE version 16.1 (StataCorp). The Fisher Exact test, McNemar exact test, Mann-Whitney test and Wilcoxon test were used for data analysis.

RESULTS

32.4% of the participants in the healthy group were males (n = 12) and 67.6% were females (n = 25). Participants' age ranged from 33 years old to 76 years old with the majority being 40-60 years of age (62.1%) (Mean age 52.4 SD = 11.4). Heart rate variability (HRV) in response to Valsalva Maneuver, metronome breathing, standing and sustained handgrip in the infection group was lower than in the healthy group throughout the weeks. Moreover, both the HRV in response to metronome breathing and standing up showed a statistically significant difference when the mean values were compared between both groups in week 1 (p = 0.03 and p = 0.013). The prevalence of CAD was significantly higher in the infection group compared to healthy volunteers, both at the beginning of the study (p = 0.018) and at the end of follow up (p = 0.057), when all patients had been discharged.

CONCLUSIONS

CAD, as assessed by the HRV, is a common finding during the recovery period of a bacterial infection, even after 6 weeks post-hospital admission. This may increase the risk of complications and cardiovascular instability. It may therefore be of value to conduct a wider scale study to further evaluate this aspect so recommendations can be made for the cardiovascular autonomic assessment of patients while they are recovering from a bacterial infectious process.

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Effect of recurrent task-induced acute stress on task performance, vagally mediated heart rate variability, and task-evoked pupil response

Advances in wearable sensor technologies can be leveraged to investigate behavioral and physiological responses in task-induced stress environments. Reliable and valid multidimensional assessments are required to detect stress given its multidimensional nature. This study investigated the effect of recurrent task-induced acute stress on task performance, vagally mediated heart variability measures (vmHRV) and task-evoked pupillary response (TEPR). Task performance, vmHRV measures, and TEPR were collected from 32 study participants while they performed a computer-based task in a recurrent task-induced acute stress environment. Mixed-effects modeling was used to assess the sensitivity of each outcome variable to experimental conditions. Repeated measures correlation tests were used to examine associations between outcome variables. Task performance degraded under stress. vmHRV measures were lower in the stress conditions relative to the no stress conditions. TEPR was found to be higher in the stress conditions compared to the no stress conditions. Task performance was negatively associated with the vmHRV measures, and degraded task performance was linked to increased TEPR in the stress conditions. There were positive associations between vmHRV measures. TEPR was negatively associated with vmHRV measures. Although task-induced stress degrades task performance, recurrent exposure to that stress could alter this effect via habituation. Further, our findings suggest that vmHRV measures and TEPR are sensitive enough to quantify psychophysiological responses to recurrent task-induced stress.

An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters

Heart rate variability (HRV) analysis is often used to estimate human health and fitness status. More specifically, a range of parameters that express the variability in beat-to-beat intervals are calculated from electrocardiogram beat detections. Since beat detection may yield erroneous interval data, these errors travel through the processing chain and may result in misleading parameter values that can lead to incorrect conclusions. In this study, we utilized Monte Carlo simulation on real data, Kolmogorov-Smirnov tests and Bland-Altman analysis to carry out extensive analysis of the noise sensitivity of different HRV parameters. The used noise models consider Gaussian and student-t distributed noise. As a result we observed that commonly used HRV parameters (e.g. pNN50 and LF/HF ratio) are especially sensitive to noise and that all parameters show biases to some extent. We conclude that researchers should be careful when reporting different HRV parameters, consider the distributions in addition to mean values, and consider reference data if applicable. The analysis of HRV parameter sensitivity to noise and resulting biases presented in this work generalizes over a wide population and can serve as a reference and thus provide a basis for the decision about which HRV parameters to choose under similar conditions.

Unveiling the Multifaceted Problems Associated with Dysrhythmia

Dysrhythmia is a term referring to the occurrence of spontaneous and repetitive changes in potentials with parameters deviating from those considered normal. The term refers to heart anomalies but has a broader meaning. Dysrhythmias may concern the heart, neurological system, digestive system, and sensory organs. Ion currents conducted through ion channels are a universal phenomenon. The occurrence of channel abnormalities will therefore result in disorders with clinical manifestations depending on the affected tissue, but phenomena from other tissues and organs may also manifest themselves. A similar problem concerns the implementation of pharmacotherapy, the mechanism of which is related to the impact on various ion currents. Treatment in this case may cause unfavorable effects on other tissues and organs. Drugs acting through the modulation of ion currents are characterized by relatively low tissue specificity. To assess a therapy's efficacy and safety, the risk of occurrences in other tissues with similar mechanisms of action must be considered. In the present review, the focus is shifted prominently onto a comparison of abnormal electrical activity within different tissues and organs. This review includes an overview of the types of dysrhythmias and the basic techniques of clinical examination of electrophysiological disorders. It also presents a concise overview of the available pharmacotherapy in particular diseases. In addition, the authors review the relevant ion channels and their research technique based on patch clumping.

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Purposes

A meta-analysis was conducted to examine the differences in heart rate variability (HRV) between depressed patients and healthy individuals, with the purpose of providing a theoretical basis for the diagnosis of depression and the prevention of cardiovascular diseases.

Methods

To search China National Knowledge Infrastructure (CNKI), WanFang, VIP, PubMed, Web of Science, Science Direct, and Cochrane Library databases to collect case-control studies on HRV in depressed patients, the retrieval date is from the establishment of the database to December 2022. Effective Public Health Practice Project (EPHPP) scale was used to evaluate literature quality, and Stata14.0 software was used for meta-analysis.

Results

This study comprised of 43 papers, 22 written in Chinese and 21 in English, that included 2,359 subjects in the depression group and 3,547 in the healthy control group. Meta-analysis results showed that compared with the healthy control group, patients with depression had lower SDNN [Hedges' g = -0.87, 95% CI (-1.14, -0.60), Z = -6.254, p < 0.01], RMSSD [Hedges' g = -0.51, 95% CI (-0.69,-0.33), Z = -5.525, p < 0.01], PNN50 [Hedges' g = -0.43, 95% CI (-0.59, -0.27), Z = -5.245, p < 0.01], LF [Hedges' g = -0.34, 95% CI (-0.55, - 0.13), Z = -3.104, p < 0.01], and HF [Hedges' g = -0.51, 95% CI (-0.69, -0.33), Z = -5.669 p < 0.01], and LF/HF [Hedges' g = -0.05, 95% CI (-0.27, 0.18), Z = -0.410, p = 0.682] showed no significant difference.

Conclusion

This research revealed that HRV measures of depressed individuals were lower than those of the healthy population, except for LF/HF, suggesting that people with depression may be more at risk of cardiovascular diseases than the healthy population.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters

Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection's sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal $ X\left(i\right) $ was band-pass filtered (5-35 Hz) to obtain a preprocessed signal $ Y\left(i\right) $. Second, $ Y\left(i\right) $ was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal $ W\left(i\right) $ by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, $ Y\left(i\right) $ was used to generate QRS template $ T\left(n\right) $ automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between $ T\left(n\right) $ and $ Y\left(i\right) $. The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.

Analysis of time-domain indices, frequency domain measures of heart rate variability derived from ECG waveform and pulse-wave-related HRV among overweight individuals: an observational study

Background: Research on the compatibility of time domain indices, frequency domain measurements of heart rate variability obtained from electrocardiogram (ECG) waveforms, and pulse wave signal (pulse rate variability; PRV) features is ongoing. The promising marker of cardiac autonomic function is heart rate variability. Recent research has looked at various other physiological markers, leading to the emergence of pulse rate variability. The pulse wave signal can be studied for variations to understand better changes in arterial stiffness and compliance, which are key indicators of cardiovascular health. Methods: 35 healthy overweight people were included. The Lead II electrocardiogram (ECG) signal was transmitted through an analog-to-digital converter (PowerLab 8/35 software, AD Instruments Pty. Ltd., New South Wales, Australia). This signal was utilized to compute Heart Rate Variability (HRV) and was sampled at a rate of 1024 Hz. The same AD equipment was also used to capture a pulse signal simultaneously. The right index finger was used as the recording site for the pulse signal using photoplethysmography (PPG) technology. Results: The participants' demographic data show that the mean age was 23.14 + 5.27 years, the mean weight was 73.68 +  7.40 kg, the mean body fat percentage was 32.23   +  5.30, and the mean visceral fat percentage was 4.60   +  2.0. The findings revealed no noticeable difference between the median values of heart rate variability (HRV) and PRV. Additionally, a strong correlation was observed between HRV and PRV. However, poor agreement was observed in the measurement of PRV and HRV. Conclusion: All indices of HRV showed a greater correlation with PRV. However, the level of agreement between HRV and PRV measurement was poor. Hence, HRV cannot be replaced with PRV and vice-versa.

Temporal complexity in photoplethysmography and its influence on blood pressure

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

Analysis of Heart Rate Variability during the Performance of the Wim Hof Method in Healthy Subjects

Cardiorespiratory interaction is related to the heart rate variability (HRV) synchronized with respiration. These metrics help to comprehend the autonomic nervous system (ANS) functionality in cardiovascular mechanisms. In this work, we aim to study the HRV in healthy subjects aged 18-24 years during the breathing techniques based on deep breaths followed by apnoeas, developed by Wim Hof (WHM). The attributes of all participates have been treated as a group and therefore, separated by gender. A total of 11 intervals have been distinguished: starting of basal respiration (SRI = 1), controlled deep breaths (CDB = 3), long expiratory apnoea (LEA = 3), short inspiratory apnoea (SIA = 3) and ending with basal respiration again (FRI = 1). To strengthen the HRV knowledge extraction from these scenarios, time and frequency analysis is conducted. In general, breathing and apnoea intervals presented significant statistically differences (p < 0.05), heart rate (HR) mean between SRI and FRI (p < 0.001), RR variability of LEA intervals (p < 0.01), root mean square of RR intervals during CDB (p < 0.05), maximum high frequency (HF) peak amplitude between SRI and FRI (p = 0.016), and low frequency (LF) area for LEA intervals (p < 0.001). When performing the frequency analysis, it has been observed that the sympathetic nervous system (SNS) has a higher contribution in the apnoea intervals. In conclusion, the WHM method implementation seems to involve a decrease in the HR. Specific breathing techniques could help to control the body in different conditions.Clinical Relevance- The WHM seems to imply a decrease on HR. Furthermore, after the implementation of the WHM, women presented higher HRV.

The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review

Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO₂), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO₂ calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO₂ across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Can HRV Predict Prolonged Hospitalization and Favorable or Unfavorable Short-Term Outcome in Patients with Acute Ischemic Stroke?

The aim of this study was to assess whether the heart rate variability (HRV) could predict a favorable or unfavorable stroke outcome. The endpoint was based on the National Institutes of Health Stroke Scale (NIHSS). The patient's health condition was assessed upon discharge from the hospital. An unfavorable stroke outcome was defined as death or NIHSS ≥ 9, while NIHSS < 9 meant a favorable stroke outcome. The studied group consisted of 59 patients with acute ischemic stroke AIS (mean age of 65.6 ± 13.2; 58% were females). An original and innovative non-linear measure was used to analyze HRV. It was based on symbolic dynamics consisting of comparing the "length of the longest words" in the night recording of HRV. "The length of the longest word" meant the longest sequence of identical adjacent symbols possible for a patient. An unfavorable stroke outcome occurred in 22 patients, whereas the majority of patients (37) had a favorable stroke outcome. The average hospitalization time of patients with clinical progression was 29 ± 14 days, and with favorable outcomes was 10 ± 3 days. Patients with long words (more than 150 adjacent RR intervals having the same symbol) were hospitalized no longer than 14 days and they had no clinical progression. The patients with a favorable stroke outcome were characterized by longer words. Our pilot study may be the beginning of work on the development of a non-linear, symbolic method as a predictor of prolonged hospitalization and increased risk of clinical progression in patients with AIS.

Hourly personal temperature exposure and heart rate variability: A multi-center panel study in populations at intermediate to high-risk of cardiovascular disease

BACKGROUND

Several studies reported temperature exposure was associated with altered cardiac automatic function, while this effect of temperature on hourly heart rate variability (HRV) among populations with cardiovascular risks was seldom addressed.

METHODS

We conducted this panel study in four Chinese cities with three repeated visits among 296 participants at intermediate to high-risk of cardiovascular disease (CVD). Real-time temperature level and 24-h ambulatory electrocardiogram were monitored during each seasonal visit. Linear mixed-effects models were used to investigate associations between individual temperature and HRV parameters, and the seasonal effects and circadian effect were also evaluated.

RESULTS

We found the overall downward trend of hourly HRV associated with acute exposure to higher temperature. For each 1 °C increment in temperature of 1-3 h prior to HRV measurements (lag 1-3 h), hourly standard deviation of normal-to-normal intervals (SDNN) decreased by 0.38% (95% confidence interval [CI]: 0.22, 0.54), 0.28% (95% CI: 0.12, 0.44), and 0.20% (95% CI: 0.04, 0.36), respectively. Similar inverse associations between temperature and HRV were observed in stratified analyses by temperature level. Inverse associations for cold and warm seasons were also observed, despite some effects gradually decreased and reversed in the warm season as lag times extended. Moreover, HRV showed a more significant reduction with increased temperature during daytime than nighttime. Percent change of hourly SDNN was -0.41% (95% CI: -0.62, -0.21) with 1 °C increment of lag 1 h during daytime, while few obvious changes were revealed during nighttime.

CONCLUSIONS

Generally, increasing temperature was significantly associated with reduced HRV. Inverse relationships for cold and warm seasons were also observed. Associations during daytime were much more prominent than nighttime. Our findings clarified the relationship of temperature with HRV and provided evidence for prevention approaches to alleviate cardiac automatic dysfunction among populations at intermediate to high-risk of CVD.

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.

Peripheral blood flow estimated by laser doppler flowmetry provides additional information about sleep state beyond that provided by pulse rate variability

Pulse rate variability (PRV), derived from Laser Doppler flowmetry (LDF) or photoplethysmography, has recently become widely used for sleep state assessment, although it cannot identify all the sleep stages. Peripheral blood flow (BF), also estimated by LDF, may be modulated by sleep stages; however, few studies have explored its potential for assessing sleep state. Thus, we aimed to investigate whether peripheral BF could provide information about sleep stages, and thus improve sleep state assessment. We performed electrocardiography and simultaneously recorded BF signals by LDF from the right-index finger and ear concha of 45 healthy participants (13 women; mean age, 22.5 ± 3.4 years) during one night of polysomnographic recording. Time- and frequency-domain parameters of peripheral BF, and time-domain, frequency-domain, and non-linear indices of PRV and heart rate variability (HRV) were calculated. Finger-BF parameters in the time and frequency domains provided information about different sleep stages, some of which (such as the difference between N1 and rapid eye movement sleep) were not revealed by finger-PRV. In addition, finger-PRV patterns and HRV patterns were similar for most parameters. Further, both finger- and ear-BF results showed 0.2-0.3 Hz oscillations that varied with sleep stages, with a significant increase in N3, suggesting a modulation of respiration within this frequency band. These results showed that peripheral BF could provide information for different sleep stages, some of which was complementary to the information provided by PRV. Furthermore, the combination of peripheral BF and PRV may be more advantageous than HRV alone in assessing sleep states and related autonomic nervous activity.

ADABase: A Multimodal Dataset for Cognitive Load Estimation

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Short-Term Outcome of Unilateral Inspiration-Coupled Hypoglossal Nerve Stimulation in Patients with Obstructive Sleep Apnea

Hypoglossal nerve stimulation (HGNS) is a therapeutic option for patients with obstructive sleep apnea (OSA) and intolerance of positive airway pressure (PAP) therapy. Most reported data are based on multicentre pivotal trials with selected baseline core clinical features. Our aim was to investigate polysomnography (PSG)-based outcomes of HGNS-therapy in a patient cohort with higher average AHI and BMI than previously reported. Data of 29 consecutive patients (nine female; mean age: 55.52 ± 8.6 years, mean BMI 30.13 ± 3.93 kg/m²) were retrospectively evaluated. Numerical values of PSG- based metrics were compared before and after intervention using Wilcoxon's rank-sum test. AHI (38.57/h ± 12.71, 24.43/h ± 13.3, p < 0.001), hypopnea index (24.05/h ± 9.4, 15.27/h ± 8.23, p < 0.001), apnea index (14.5/h ± 12.05, 9.17/h ± 10.86, p < 0.01), snoring index (262.68/h ± 170.35, 143.48/h ± 162.79, p < 0.001), cortical arousal index (20.8/h ± 10.34 vs. 14.9/h ± 8.36, p < 0.01) and cumulative duration of apnea and hypopnea during sleep (79.79 min ± 40.32 vs. 48.62 min ± 30.56, p < 0.001) were significantly lower after HGNS. HGNS provides an effective therapy option for selected patients not tolerating PAP-therapy with higher average AHI and BMI than usually reported. HGNS-therapy appears to suppress central nervous system arousal circuits while not eliciting peripheral autonomous sympathetic activation. Such metrics as the snoring index and the cumulative duration of respiratory events during sleep may be considered in future HGNS studies.

A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability

BACKGROUND

Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions.

OBJECTIVE

We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor.

METHODS

We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods.

RESULTS

We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances.

CONCLUSION

The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.

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.

Emotion Recognition: Photoplethysmography and Electrocardiography in Comparison

Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests.

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.

Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals

Pulse rate variability (PRV) has been proposed as a surrogate for the estimation of Heart Rate Variability (HRV), which is a non-invasive technique used to assess the cardiac autonomic activity. However, both physiological and technical factors may affect the relationship between HRV and PRV, and there are no standards for the analysis of PRV from photoplethysmographic (PPG) signals. The aim of this study was to determine the best outlier management strategies for PRV analysis. 117 PPG signals with randomly generated PRV information were simulated using Gaussian signals. From these, interbeat intervals were detected and different outlier detection and correction techniques were applied. Time and frequency-domain and non-linear PRV indices were extracted and compared with respect to the gold standard values obtained from the simulated PRV information. The results show that, in good quality PPG signals, there is no need to apply any outlier management technique for the extraction of PRV information. Clinical relevance- Establishing guidelines for PRV mea-surement can lead to more reliable and comparable results, as well as to the increase in the use of this variable for the diagnosis and monitoring of cardiovascular and autonomic conditions.

Estimation of Heart Rate Variability Parameters by Machine Learning Approaches Applied to Facial Infrared Thermal Imaging

Heart rate variability (HRV) is a reliable tool for the evaluation of several physiological factors modulating the heart rate (HR). Importantly, variations of HRV parameters may be indicative of cardiac diseases and altered psychophysiological conditions. Recently, several studies focused on procedures for contactless HR measurements from facial videos. However, the performances of these methods decrease when illumination is poor. Infrared thermography (IRT) could be useful to overcome this limitation. In fact, IRT can measure the infrared radiations emitted by the skin, working properly even in no visible light illumination conditions. This study investigated the capability of facial IRT to estimate HRV parameters through a face tracking algorithm and a cross-validated machine learning approach, employing photoplethysmography (PPG) as the gold standard for the HR evaluation. The results demonstrated a good capability of facial IRT in estimating HRV parameters. Particularly, strong correlations between the estimated and measured HR (r = 0.7), RR intervals (r = 0.67), TINN (r = 0.71), and pNN50 (%) (r = 0.70) were found, whereas moderate correlations for RMSSD (r = 0.58), SDNN (r = 0.44), and LF/HF (r = 0.48) were discovered. The proposed procedure allows for a contactless estimation of the HRV that could be beneficial for evaluating both cardiac and general health status in subjects or conditions where contact probe sensors cannot be used.

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.

Applicability of the single-layer flat-coil-oscillator technology-based vibration and vibro-acoustic sensors in medical and biological study of the cardiovascular system: Advantages and perspectives of the carotid pulse wave registration

The possibility and feasibility of using the single-layer flat-coil-oscillator (SFCO) technology-based vibration and vibro-acoustic sensors in diagnostic devices and biomedical studies of the cardiovascular system are discussed in this paper. Using an example of recording pulse waves of left carotid artery and their analysis, the information content of the data recorded by these sensors in a number of cases is shown-assessment of age-related changes in the stiffness of the vascular wall, assessment of the dynamics of systolic volume, reflecting myocardial contractility, and rhythm disturbance (extra-systole and arrhythmia). These sensors are shown to be promising in recording heart sounds due to their high sensitivity. The possibility of assessing the dynamics of relaxation of the cardiovascular system after exercise (stress test) is shown. The advantages of using SFCO vibration and vibro-acoustic sensors are high sensitivity, ease of use, and no need to train specialists. These advantages open new perspectives for their implementation in mobile wearable "smart" devices for individual monitoring.

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.

Validity and reliability of different smartphones applications to measure HRV during short and ultra-short measurements in elite athletes

BACKGROUND AND OBJECTIVE

Heart rate variability (HRV) has been proposed as a useful marker that can show the performance adaptation and optimize the training process in elite athletes. The development of wearable technology permits the measurement of this marker through smartphone applications. The purpose of this study is to assess the validity and reliability of short and ultra-short HRV measurements in elite cyclists using different smartphone applications.

METHOD

Twenty-six professional cyclists were measured at rest in supine and in seated positions through the simultaneous use of an electrocardiogram and two different smartphone applications that implement different technologies to measure HRV: Elite HRV (with a chest strap) and Welltory (photoplethysmography). Level of significance was set at p < 0.05.

RESULTS

Compared to an electrocardiogram, Elite HRV and Welltory showed no differences neither in supine nor in seated positions (p > 0.05) and they showed very strong to almost perfect correlation levels (r = 0.77 to 0.94). Furthermore, no differences were found between short (5 min) and ultra-short (1 min) length measurements. Intraclass correlation coefficient showed good to excellent reliability and the standard error of measurement remained lower than 6%.

CONCLUSION

Both smartphone applications can be implemented to monitor HRV using short- and ultra-short length measurements in elite endurance athletes.

A Review of Recent Developments in Driver Drowsiness Detection Systems

Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.