Comparison between Electrocardiographic and Earlobe Pulse Photoplethysmographic Detection for Evaluating Heart Rate Variability in Healthy Subjects in Short- and Long-Term Recordings

Pilot randomized controlled trial of biofeedback on reducing psychological and physiological stress among persons experiencing homelessness

People experiencing homelessness report increased exposure to traumatic life events and higher rates of depression, anxiety, and post-traumatic stress disorder as compared with the general population. Heart rate variability-biofeedback (HRV-BF) has been shown to decrease symptoms of stress, anxiety, depression, and PTSD. However, HRV-BF has not been tested with the most vulnerable of populations, homeless adults. The purpose of this randomized controlled trial was to compare the effectiveness of an HRV-BF intervention versus a Health Promotion (HP) active control intervention focused on improving mental health symptoms among homeless adults. Guided by a community advisory board, homeless adults residing in Skid Row, Los Angeles (n = 40) were randomized to either the HRV-BF or an active HP control group and received eight weekly, 30-min sessions over two months, delivered by a nurse-led community health worker team. Dependent variables of HRV, mental health, anxiety, depression, and PTSD were measured at baseline, the 8-week session, and/or 2-month follow-up. All intervention sessions were completed by 90% (36/40) of participants. Both the HRV-BF and HP interventions showed significant increases in HRV from baseline to 2-month follow-up, with no significant difference between the intervention groups. The HRV-BF programme revealed a somewhat greater, although non-significant, improvement in anxiety, depression, and PTSD symptoms than the HP programme. The usefulness of both interventions, focused on emotional and physical health, warrants future studies to examine the value of a combined HRV-BF and HP intervention.

A comprehensive evaluation of linear and non-linear HRV parameters between paced breathing and stressful mental state

Background

Heart rate variability (HRV) is a crucial metric that provides valuable insight into the balance between relaxation and stress. Previous research has shown that most HRV parameters improve during periods of mental relaxation, while decreasing during tasks involving cognitive workload. Although a comprehensive analysis of both linear and non-linear HRV parameters has been carried out in existing literature, there still exists a need for further research in this area. Additionally, limited knowledge exists regarding how specific interventions may influence the interpretation of these parameters and how the different parameters correlate under different interventions. This study aims to address these gaps by conducting a thorough comparison of different linear and non-linear HRV parameters under mentally relaxed versus stressful states.

Methodology

Participants were randomly and equally divided among two between-subjects groups: relaxed-stress (RS) (N = 22) and stress-relaxed (SR) (N = 22). In the RS group, a paced breathing task was given for 5 min to create relaxation, and was followed by a 5-min time-based mental calculation task to create stress. In the SR group, the order of the stress and relaxed tasks was reversed. There was a washout period of 15 min after the first task in both groups.

Results

Of the 37 HRV parameters, 33 differed significantly between the two interventions. The majority of the parameters exhibited an improving and degrading tendency of HRV parameters in the relaxed and stressed states, respectively. The correlation of the majority of HRV parameters decreases during stress, while prominent time domain and geometric domain parameters stand out in the correlation.

Conclusion

Overall, HRV parameters can be reliably used to assess a person's relaxed and stressed mental states during paced breathing and mental arithmetic task respectively. Furthermore, non-linear HRV parameters provide accurate estimators of the mental state, in addition to the commonly used linear parameters.

Investigating heart rate variability measures during pregnancy as predictors of postpartum depression and anxiety: an exploratory study

Perinatal affective disorders are common, but standard screening measures reliant on subjective self-reports might not be sufficient to identify pregnant women at-risk for developing postpartum depression and anxiety. Lower heart rate variability (HRV) has been shown to be associated with affective disorders. The current exploratory study aimed to evaluate the predictive utility of late pregnancy HRV measurements of postpartum affective symptoms. A subset of participants from the BASIC study (Uppsala, Sweden) took part in a sub-study at pregnancy week 38 where HRV was measured before and after a mild stressor (n = 122). Outcome measures were 6-week postpartum depression and anxiety symptoms as quantified by the Edinburgh Postnatal Depression Scale (EPDS) and the Beck Anxiety Inventory (BAI). In total, 112 women were included in a depression outcome analysis and 106 women were included in an anxiety outcome analysis. Group comparisons indicated that lower pregnancy HRV was associated with depressive or anxious symptomatology at 6 weeks postpartum. Elastic net logistic regression analyses indicated that HRV indices alone were not predictive of postpartum depression or anxiety outcomes, but HRV indices were selected as predictors in a combined model with background and pregnancy variables. ROC curves for the combined models gave an area under the curve (AUC) of 0.93 for the depression outcome and an AUC of 0.83 for the anxiety outcome. HRV indices predictive of postpartum depression generally differed from those predictive of postpartum anxiety. HRV indices did not significantly improve prediction models comprised of psychological measures only in women with pregnancy depression or anxiety.

Accuracy and clinical utility of heart rate variability derived from a wearable heart rate monitor in patients undergoing major abdominal surgery

Low heart rate variability (HRV) can potentially identify patients at risk of intraoperative hypotension. However, it is unclear whether cheaper, readily accessible consumer heart rate (HR) monitors can provide similar utility to clinical Holter electrocardiograph (ECG) monitors. The objectives of this study were (1) to assess the validity of using the Polar H10 HR monitor as an alternative to a clinical Holter ECG and (2) to test total power (TP) as a predictor of intraoperative hypotension. The primary outcome was the level of agreement between Polar H10 and Holter ECG. Twenty-three patients undergoing major abdominal surgery with general anesthesia had 5-minute HR recordings taken concurrently with both devices during a pre-anesthetic consultation. Agreement between Polar H10 and Holter ECG was compared via Bland-Altman analysis and Lin's Concordance Correlation Coefficient. Patients were divided into groups based on TP <  500 m s 2 and TP >  500 m s 2 . Intraoperative hypotension was defined as MAP < 60 mmHg, systolic blood pressure < 80 mmHg, or 35% decrease in MAP from baseline. There was substantial agreement between Polar H10 and Holter ECG for average R-R interval, TP and other HRV indices. Reduced TP (< 500 ms 2 ) had a high sensitivity (80%) and specificity (100%) in predicting intraoperative hypotension. Patients with reduced TP were significantly more likely to require vasoactive drugs to maintain blood pressure.The substantial agreement between Polar H10 and Holter ECG may justify its use clinically. The use of preoperative recordings of HRV has the potential to become part of routine preoperative assessment as a useful screening tool to predict hemodynamic instability in patients undergoing general anesthesia.

Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study

Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.

Smartwatch-derived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease

Aims

We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease.

Methods and results

We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin's concordance correlation coefficient (ρc) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (ρc = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; ρc = 0.9617), and very low frequency power (VLF power; ρc = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; ρc = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; ρc = 0.6617) showed only moderate concordance.

Conclusion

Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Comparison of pulse rate variability from post-auricula and heart rate variability during different body states for healthy subjects

Heart rate variability (HRV) extracted from the electrocardiogram (ECG) is an essential indicator for assessing the autonomic nervous system in clinical. Some scholars have studied the feasibility of pulse rate variability (PRV) instead of HRV. However, there is little qualitative research in different body states. In this paper, the photoplethysmography (PPG) of postauricular and finger and the ECG of fifteen subjects were synchronously collected for comparative analysis. The eleven experiments were designed according to the daily living state, including the stationary state, limb movement state, and facial movement state. The substitutability of nine variables was investigated in the time, frequency, and nonlinearity domain by Passing Bablok regression and Bland Altman analysis. The results showed that the PPG of the finger was destroyed in the limb movement state. There were six variables of postauricular PRV, which showed a positive linear relationship and good agreement (p > 0.05, ratio ≤0.2) with HRV in all experiments. Our study suggests that the postauricular PPG could retain the necessary information of the pulse signal under the limb movement state and facial movement state. Therefore, postauricular PPG could be a better substitute for HRV, daily PPG detection, and mobile health than finger PPG.

Heart Rate Estimation from Incomplete Electrocardiography Signals

As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60−120 bpm in the database without significant arrhythmias and a corresponding range of 30−150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder

Background and purpose: Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. Methods: Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. Results: Cardiac autonomic indices had low performances (accuracy 63-69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. Conclusions: Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD.

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.

Comparison of Apple Watch Series 4 vs. KardiaMobile: A Tale of Two Devices

Background

The Apple Watch Series 4 (AW4) and the KardiaMobile single bipolar lead model (KM) are 2 of the most popular US Food & Drug Administration (FDA)-approved commercial heart trackers. However, a lack of knowledge remains regarding their rhythm-detection accuracy in real-life clinical situations. This paper aims to determine the practicality of using an AW4 or a KM in modern medical practice, by assessing the accuracy of each in identifying heart rhythms and heart rate.

Methods

Participants from the Toronto Heart Centre clinic were enrolled from January 2019 to December 2019. They had a 12-lead electrocardiogram (ECG), followed by wearing the AW4 watch (OS 5.3), and pressing on the KM electrode plates, within the span of 5 minutes of one another. Each session involved a 12-lead ECG, an ECG from each device, and AW4’s photoplethysmography function (APPG).

Results

Of 200 participants, 162 (81%) were in sinus rhythm, and 38 (19%) had atrial fibrillation. The rhythm-detection accuracy for sinus rhythm was 100% for the AW4, and 99.03% for the KM. For atrial fibrillation, accuracy was 90.48% for the AW4, and 100% for the KM. The heart rate accuracy for sinus rhythm was 94.39% for the KM, 90.65% for the APPG, and 96.26% for the Apple ECG function. The heart rate accuracy for atrial fibrillation was 91.30% for the KM, 82.61% for the APPG, and 86.96% for the Apple ECG function.

Conclusions

Both the AW4 and the KM could reliably detect rhythm and heart rate in real-life clinical situations. However, a nonsignificant trend occurred toward better rhythm detection and accuracy with KM, compared with AW4. The difference is mainly due to artifacts (eg, tremors) and the fit of the strap for AW4. The findings have important implications for how these consumer devices can be used in real-life clinical settings.

Real-Time Stress Analysis Affecting Nurse during Elective Spinal Surgery Using a Wearable Device

Successful spinal surgery demands high levels of concentration and cooperation from participating health care workers. The intraoperative stress levels and concentration levels of surgeons have been studied previously; however, those of nurses are rarely studied. Therefore, the purpose of this study is to understand the stresses affecting surgical nurses by their participating role during spinal surgery. A total of 160 surgical stress records were obtained during 40 surgeries, including electroencephalography (EEG) signals and heart rate variability (HRV) from three orthopedic spinal surgeons and six nurses; concentration, tension level and physical stress were analyzed. Levels of both concentration and tension were significantly higher in circulating nurses during all surgical stages (p < 0.05). Both beats per minute and low frequency/high frequency ratios, which reflect physical stress, were higher in scrub nurses (p < 0.05). As the surgical experience of scrub nurses increased, the key parameters related to stress tended to decrease (p < 0.01). These results will contribute to understanding the pattern of intraoperative stress of surgical nurses, and therefore help in enhancing the teamwork of the surgical team for optimal outcomes.

Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation

Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.

Causality in cardiorespiratory signals in pediatric cardiac patients

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method. Clinical relevance- The presented methods (among which NNGC seems to be the most valid) allow for quantification of RSA and study of dependency between tidal volume and RR intervals which may help to better understand association between respiratory and cardiovascular systems in different populations.

Dynamic Phase Extraction: Applications in Pulse Rate Variability

Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between inter-beat interval, IBI, and breathing has very rarely been studied. Develop a technique by which the phase relationship between IBI and breathing can be accurately and efficiently extracted from photoplethysmography (PPG) data. A program based on Lock-in Amplifier technology was written in Python to implement a novel technique, Dynamic Phase Extraction. It was tested using a breath pacer and a PPG sensor on 6 subjects who followed a breath pacer at varied breathing rates. The data were then analyzed using both traditional methods and the novel technique (Dynamic Phase Extraction) utilizing a breath pacer. Pulse data was extracted using a PPG sensor. Dynamic Phase Extraction (DPE) gave the magnitudes of the variation in IBI associated with breathing [Formula: see text] measured with photoplethysmography during paced breathing (with premature ventricular contractions, abnormal arrhythmias, and other artifacts edited out). [Formula: see text] correlated well with two standard measures of pulse rate variability: the Standard Deviation of the inter-beat interval (SDNN) (ρ = 0.911) and with the integrated value of the Power Spectral Density between 0.04 and 0.15 Hz (Low Frequency Power or LF Power) (ρ = 0.885). These correlations were comparable to the correlation between the SDNN and the LF Power (ρ = 0.877). In addition to the magnitude [Formula: see text], Dynamic Phase Extraction also gave the phase between the breath pacer and the changes in the inter-beat interval (IBI) due to respiratory sinus arrythmia (RSA), and correlated well with the phase extracted using a Fourier transform (ρ = 0.857). Dynamic Phase Extraction can extract both the phase between the breath pacer and the changes in IBI due to the respiratory sinus arrhythmia component of pulse rate variability ([Formula: see text], but is limited by needing a breath pacer.

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.

Heart Rate Variability during Auricular Acupressure at Heart Point in Healthy Volunteers: A Pilot Study

Heart rate variability (HRV) is the variation in time between each heartbeat. Increasing HRV may contribute to improving autonomic nervous system dysfunctions. Acupuncture stimulation through the vagus plexus in the ear is considered as a method that can improve HRV. In this pilot study, we examined 114 healthy volunteers at the Faculty of Traditional Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, from January to May 2020. During a 20-minute interval, participants were stimulated two times at the acupoint in the left ear with Semen seed. The heart rate and HRV values were monitored before, during, and after acupressure every 5 minutes. When we compared the experimental group with the control group, HRV significantly increased in the stage of ear-stimulated acupressure compared with the stage before and after the auricular acupressure (p=0.01, p=0.04, p=0.04 and p=0.02) and the difference was not statistically significant compared with the phase of nonstimulated (p=0.15, p=0.28). The changes in other values including SDNN (standard deviation of the average NN), RMSSD (root mean square of successive RR interval differences), LF (low-frequency power), and HF (high-frequency power) in all stages were not statistically significant (p=>0.05) between groups. Based on the results, we can determine the increase in HRV when conducting auricular acupressure with stimulation at the heart acupoint on the left ear. This leads to a direction in further studies for clinical application for patients with autonomic nervous disorder.

Machine Learning Based Real-Time Diagnosis of Mental Stress Using Photoplethysmography

Mental stress is a natural response to life activities. However, acute and prolonged stress may cause psychological and heart diseases. Heart rate variability (HRV) is considered an indicator of mental stress and physical fitness. The standard way of obtaining HRV is using electrocardiography (ECG) as the time interval between two consecutive R-peaks. ECG signal is collected by attaching electrodes on different locations of the body, which need a proper clinical setup and is costly as well; therefore, it is not feasible to monitor stress with ECG. Photoplethysmography (PPG) is considered an alternative for mental stress detection using pulse rate variability (PRV), the time interval between two successive peaks of PPG. This study aims to diagnose daily life stress using low-cost portable PPG devices instead of lab trials and expensive devices. Data is collected from 27 subjects both in rest and in stressed conditions in daily life routine. Thirty-six time domain, frequency domain, and non-linear features are extracted from PRV. Multiple machine learning classifiers are used to classify these features. Recursive feature elimination, student t-test and genetic algorithm are used to select these features. An accuracy of 72% is achieved using stratified leave out cross-validation using K-Nearest Neighbor, and it increased up to 81% using a genetic algorithm. Once the model is trained with the best features selected with the genetic algorithm, we used the trained weights for the real-time prediction of mental stress. The results show that using a low-cost device; stress can be diagnosed in real life. The proposed method enable the regular monitoring of stress in short time that help to control the occurrence of psychological and cardiovascular diseases.

Improvements in Medical System Safety Analytics for Authentic Measure of Vital Signs Using Fault-Tolerant Design Approach

The study presents a novel design method that improves system availability using fault-tolerant features in a non-invasive medical diagnostic system. This approach addresses the effective detection of functional faults, improves the uninterruptible system operating period with reduced false alarms, and provides an authentic measure of vital cardiac signs using diverse multimodal sensing elements like the photoplethysmogram (PPG) and the ECG. Most systems rely on a 1oo1 (one-out-of-one) design method, which inherently limits accuracy in existing practice. In this proposed approach, the quality of segregated authentic vital sign measured values could tremendously benefit the performance of resourceful nursing with negligible alarm fatigue and predict illness more accurately. The system builds upon the selected 2oo2 (two-out-of-two) safety-related design architecture and is evaluated with implemented functions like the fault detection and identification logic, the correlation coefficient-based safety function, and the fault-tolerant safe degradation switching mechanism for accurate measurements. The system was tested on 50 adults of various age groups. The analyzed captured data showed highly accurate vital sign data in this fault-tolerant approach with reduced false alarms. The proposed design method evaluated safety-related mechanisms along with a combination of the same and diverse sensors in a medical monitoring device, showing more reliable functioning of the system and authentic data for better nursing. This design approach showed a 45–55% increased improvement in system availability, thus allowing for accurate and uninterruptable tracking of vital signs for better nursing during critical times in the ICU.

The Effects of Soundscapes in Relieving Stress in an Urban Park

Urban parks are important urban public spaces that guarantee people recreation, create positive emotions and relieve stress. Emerging research has shown that natural soundscapes are associated with restorative landscapes in urban parks. However, there is still a lack of knowledge on the use of physiological indexes to evaluate the effects of natural sounds versus human-based sounds on stress relief. In this study, the three physiological indexes of skin conductance level, heart rate and heart rate variability were collected in Fuzhou West Lake Park with the help of Ergo LAB data platform, and a soundscape perception evaluation questionnaire was used to assess the degree of soundscape perceptions in the sample sites. The differences in the stress relieving effects of different urban park environments were analysed by applying the median test, the Wilcoxon test was applied to analyse the effects of soundscapes and urban park environments on relieving stress, and regression analysis was used to identify the important factors of restorative soundscapes. The results found that urban park environments provide a certain degree of stress relief, but the stress relieving effects of different urban park environments vary and that natural spaces play an important role in relieving stress. Urban park soundscapes are key to restorative environmental design, with natural sounds such as birdsong and stream sound being important factors of restorative soundscapes.

Which Factors Affect the Stress of Intraoperative Orthopedic Surgeons by Using Electroencephalography Signals and Heart Rate Variability?

Can we recognize intraoperative real-time stress of orthopedic surgeons and which factors affect the stress of intraoperative orthopedic surgeons with EEG and HRV? From June 2018 to November 2018, 265 consecutive records of intraoperative stress measures for orthopedic surgeons were compared. Intraoperative EEG waves and HRV, comprising beats per minute (BPM) and low frequency (LF)/high frequency (HF) ratio were gathered for stress-associated parameters. Differences in stress parameters according to the experience of surgeons, intraoperative blood loss, and operation time depending on whether or not a tourniquet were investigated. Stress-associated EEG signals including beta 3 waves were significantly higher compared to EEG at rest for novice surgeons as the procedure progressed. Among senior surgeons, the LF/HF ratio reflecting the physical demands of stress was higher than that of novice surgeons at all stages. In surgeries including tourniquets, operation time was positively correlated with stress parameters including beta 1, beta 2, beta 3 waves and BPM. In non-tourniquet orthopedic surgeries, intraoperative blood loss was positively correlated with beta 1, beta 2, and beta 3 waves. Among orthopedic surgeons, those with less experience demonstrated relatively higher levels of stress during surgery. Prolonged operation time or excessive intraoperative blood loss appear to be contributing factors that increase stress.

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Heart rate variability in late pregnancy: exploration of distinctive patterns in relation to maternal mental health

Exploration of photoplethysmography (PPG), a technique that can be translated to the clinic, has the potential to assess the autonomic nervous system (ANS) through heart rate variable (HRV) in pregnant individuals. This novel study explores the complexity of mental health of individuals in a clinical sample responding to a task in late pregnancy; finding those with several types of past or current anxiety disorders, greater trait anxiety, or greater exposure to childhood traumatic events had significantly different HRV findings from the others in the cohort. Lower high frequency (HF), a measure of parasympathetic activity, was found for women who met the criteria for the history of obsessive-compulsive disorder (OCD) (p = 0.004) compared with women who did not meet the criteria for OCD, and for women exposed to greater than five childhood traumatic events (p = 0.006) compared with those exposed to four or less childhood traumatic events. Conversely higher low frequency (LF), a measure thought to be impacted by sympathetic system effects, and the LF/HF ratio was found for those meeting criteria for a panic disorder (p = 0.006), meeting criteria for social phobia (p = 0.002), had elevated trait anxiety (p = 0.006), or exposure to greater than five childhood traumatic events (p = 0.004). This study indicates further research is needed to understand the role of PPG and in assessing ANS functioning in late pregnancy. Study of the impact of lower parasympathetic functioning and higher sympathetic functioning separately and in conjunction at baseline and in relation to tasks during late pregnancy has the potential to identify individuals that require more support and direct intervention.

Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative of fatigue, overtraining or health issues. There has been a surge in wearable devices that claim to measure HRV. Some of these include spot measurements, whilst others only record during periods of rest and/or sleep. Few are capable of continuously measuring HRV (≥24 h). We undertook a narrative review of the literature with the aim to determine which currently available wearable devices are capable of measuring continuous, precise HRV measures. The review also aims to evaluate which devices would be suitable in a field setting specific to military populations. The Polar H10 appears to be the most accurate wearable device when compared to criterion measures and even appears to supersede traditional methods during exercise. However, currently, the H10 must be paired with a watch to enable the raw data to be extracted for HRV analysis if users need to avoid using an app (for security or data ownership reasons) which incurs additional cost.

Heart Rate Variability Analysis: How Much Artifact Can We Remove?

OBJECTIVE

Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.

METHODS

This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject's HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.

RESULTS

Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.

CONCLUSION

Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.

Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors

We introduce a novel approach for robust estimation of physiological parameters such as interbeat interval (IBI) and heart rate variability (HRV) from cardiac signals captured with wearable sensors in the presence of motion artifacts. Motion artifact due to physical exercise is known as a major source of noise that contributes to a significant decline in the performance of IBI and HRV estimation techniques for cardiac monitoring in free-living environments. Therefore, developing robust estimation algorithms is essential for utilization of wearable sensors in daily life situations. The proposed approach includes two algorithmic components. First, we propose a combinatorial technique to select characteristic points that define heartbeats in noisy signals in time domain. The heartbeat detection problem is defined as a shortest path search problem on a direct acyclic graph that leverages morphological features of the cardiac signals by taking advantage of the time-continuity of heartbeats - each heartbeat ends with the starting point of the next heartbeat. The graph is constructed with vertices and edges representing candidate morphological features and IBIs, respectively. Second, we propose a fusion technique to combine physiological parameters estimated from different morphological features using the shortest path algorithm to obtain more accurate IBI/HRV estimations. We evaluate our techniques on motion-corrupted photoplethysmogram and electrocardiogram signals. Our results indicate that the estimated IBIs are highly correlated with the ground truth (r = 0.89) and detected HRV parameters indicate high correlation with the true HRV parameters. Furthermore, our findings demonstrate that the developed fusion technique, which utilizes different morphological features, achieves a correlation coefficient that is at least 3% higher than that obtained using single physiological characteristic.

Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.

An Injectable System for Subcutaneous Photoplethysmography, Accelerometry, and Thermometry in Animals

Obtaining physiological data from animals in a non-obtrusive and continuous manner is important to veterinary science. This paper demonstrates the design and deployment of a miniaturized capsule-based system for subdermal injection to provide real-time and continuous heart-rate, movement, and core-body-temperature measurements. The presented device incorporates sensors for photoplethysmography, motion detection, and temperature measurements. A bluetooth-low-energy enabled microcontroller configures the sensors, digitizes the sensor information, and wirelessly connects with external devices. The device is powered by a CR425 battery for this paper, and various other battery solutions are available based upon the use case. The design uses only commercially available integrated circuits in order to reduce the development cost and be modular. The encapsulation is a combination of medical epoxy and poly(methyl methacrylate) that fits within a 6-gauge hypodermic needle. The preliminary evaluation of the device included an in vitro assessment of its thermal response and measurement accuracy, the impact of one-month implantation on surrounding tissue, the power consumption with duty cycling of various sensors, and a measurement of physiological signals in a rat and a chicken. Having a form factor and implantation method similar to existing devices for animals, this novel system is a useful platform for both scientists and veterinarians to better study a diverse range of animals.

Usefulness of cardiac parasympathetic index in CPAP-treated patients with obstructive sleep apnea: A preliminary study

Cardiac autonomic indexes, including cardiac parasympathetic index and cardiac sympathetic index, have been reported to accurately identify patients with sleep disorders such as obstructive sleep apnea. Our study aimed to assess cardiac autonomic indexes in patients with obstructive sleep apnea before and during a single full-night continuous positive airway pressure therapy using a combined approach. Our simultaneous heart rate variability-polysomnographic study included 16 never-treated obstructive sleep apnea patients. Two patients dropped out. Patients underwent combined recordings in two consecutive days, at baseline and during a single full-night of acute continuous positive airway pressure treatment. We calculated cardiac parasympathetic index and cardiac sympathetic index as night/day ratio for high-frequency and low-frequency heart rate variability spectral components, respectively. Continuous positive airway pressure treatment significantly reduced cardiac autonomic indexes values in comparison with baseline values (cardiac parasympathetic index: p < .0001; cardiac sympathetic index: p = .001). After acute continuous positive airway pressure treatment, the percentage of decrease of cardiac parasympathetic index was greater than that of cardiac sympathetic index (51.02 ± 15.72 versus 34.64 ± 26.93). A positive statistical correlation was also found between decrease of cardiac parasympathetic index and decrease of apnea-hypopnea index after continuous positive airway pressure (p < .001). This study improves the knowledge on cardiac autonomic modulation during acute continuous positive airway pressure therapy in obstructive sleep apnea. Our results demonstrate that both autonomic indexes decreased significantly after a single-night of acute continuous positive airway pressure therapy. Cardiac parasympathetic index more than cardiac sympathetic index was related to decrease of apnea-hypopnea index after continuous positive airway pressure therapy, thus representing a potential help in everyday clinical practice.

Relationship between Ischemic Stroke and Pulse Rate Variability as a Surrogate of Heart Rate Variability

Autonomic reflex ascertains cardiovascular homeostasis during standing. Impaired autonomic reflex could lead to dizziness and falls while standing; this is prevalent in stroke survivors. Pulse rate variability (PRV) has been utilized in the literature in lieu of heart rate variability (HRV) for ambulatory and portable monitoring of autonomic reflex predominantly in young, healthy individuals. Here, we compared the PRV with gold standard HRV for monitoring autonomic reflex in ischemic stroke survivors. Continuous blood pressure and electrocardiography were acquired from ischemic stroke survivors (64 ± 1 years) and age-matched controls (65 ± 2 years) during a 10-minute sit-to-stand test. Beat-by-beat heart period (represented by RR and peak-to-peak (PP) intervals), systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse arrival time (PAT), an indicator of arterial stiffness, were derived. Time and frequency domain HRV (from RR intervals) and PRV (from PP intervals) metrics were extracted. PAT was lower (248 ± 7 ms vs. 270 ± 8 ms, p < 0.05) suggesting higher arterial stiffness in stroke survivors compared to controls during standing. Further, compared to controls, the agreement between HRV and PRV was impaired in stroke survivors while standing. The study outcomes suggest that caution should be exercised when considering PRV as a surrogate of HRV for monitoring autonomic cardiovascular control while standing in stroke survivors.

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

OBJECTIVE

The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic (PPG) pulse wave signals to perform pulse rate variability (PRV) analysis as a surrogate for heart rate variability (HRV) analysis.

APPROACH

Forehead and finger PPG signals were recorded during a tilt-table test simultaneously with the electrocardiogram (ECG). Artefacts were detected and removed and five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse, defined as the intersecting tangents method. Pulse period (PP) time interval series were obtained from both PPG signals and compared with the RR intervals obtained from the ECG. HRV and PRV signals were estimated and classical time and frequency domain indices were computed.

MAIN RESULTS

The middle-amplitude point of the PPG signal (n _M ), the apex point of the first derivative ([Formula: see text]), and the tangent intersection point (n _T ) are the most suitable fiducial points for PRV analysis, resulting in the lowest relative errors estimated between PRV and HRV indices and higher correlation coefficients and reliability indices. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland-Altman analysis. Hence, these signals have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for n _M and [Formula: see text] using Friedman statistics with a Bonferroni multiple-comparison test, and we propose that n _M is the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG.

SIGNIFICANCE

The accuracy of the pulse wave detection depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point for performing a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques.

Quantification of error between the heartbeat intervals measured form photoplethysmogram and electrocardiogram by synchronisation

Currently, heartbeat intervals required for the analysis of heart rate variability (HRV) are derived from electrocardiogram (ECG). Many investigators have explored the possibility of using photoplethysmography (PPG), for the analysis of HRV. However, all these studies are based on statistical approach and have used the correlation coefficient for the comparison of HRV data obtained using ECG and PPG, which is inappropriate as the causal relationship between the R-peaks in ECG and the systolic peaks in PPG is well known in physiology. In this study, the heart beat intervals measured from PPG, are compared, beat by beat, with the corresponding beat intervals of same cardiac cycle obtained from the synchronously recorded ECG and the differences between them are taken as errors. These errors are verified to exactly match with the variations in the pulse transit times (PTTs), beat by beat. The error in the measurement of heartbeat intervals using PPG is quantified by obtaining the root mean square of the errors associated with each beat interval for a subject. The rms error, which is found to vary between 0.17 and 1.81% across the study group of 42 subjects, can be treated as insignificant, considering the nonstationary character of physiological signals. The errors are compared and interpreted with the variations in PTT. In view of these findings, PPG can be considered as a low cost, safe and more convenient alternative to ECG, as a wearable sensor outside hospital environment, for the analysis of HRV, without compromising on accuracy.

Recording Heart Rate Variability of Dairy Cows to the Cloud-Why Smartphones Provide Smart Solutions

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82⁻0.98) for cows as well as for control data set in human being (r = 0.98⁻0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.