Time-frequency analysis of heart-rate variability

Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study

BACKGROUND

Traditional electrocardiography (ECG)-derived heart rate variability (HRV) and photoplethysmography (PPG)-derived "HRV" (termed PRV) have been reported interchangeably. Any potential dissociation between HRV and PRV could be due to the variability in pulse arrival time (PAT; time between heartbeat and peripheral pulse).

OBJECTIVE

This study examined if PRV is equivalent to ECG-derived HRV and if PRV's innate error makes it a high-quality measurement separate from HRV.

METHODS

ECG data from 1084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PAT dispersions for both the wrist (n = 42) and finger (n = 49) were derived from Mol et al. (Exp Gerontol. 2020; 135: 110938). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation (σ) of the prior corresponds to the PAT dispersion from Mol et al. This was simulated 10,000 times for each PAT σ. The root mean square of successive differences (RMSSD) and standard deviation of N-N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at ± 0.2% of true HRV. The highest density interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PAT σ.

RESULTS

The lowest PAT σ (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the σ of PAT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. The HDI interval width increases with increasing PAT σ, with the HDI width increasing at a higher rate for RMSSD than SDNN.

CONCLUSIONS

For individuals with greater PAT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, SDNN may have more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.

Heart Rate Variability, Blood Pressure and Peripheral Oxygen Saturation during Yoga Adham and Mahat Breathing Techniques without Retention in Adult Practitioners

Background: Heart rate variability (HRV) is the change in time intervals between heart beats, reflecting the autonomic nervous system's ability to adapt to psychological and physiological demands. Slow breathing enhances parasympathetic activity, increasing HRV. Pranayama, a yoga breathing technique, affords the conscious regulation of respiration frequency. This study aimed to characterize HRV, blood pressure and peripheral oxygen saturation of basic yoga breathing slow techniques with regular yoga practitioners. Methods: In total, 45 yoga practitioners were included in the study (including 7 males, mean age of 54.04 ± 11.97 years) with varying levels of yoga experience (minimum 3 months, maximum 37 years). Participants performed three breathing conditions: baseline (control) and two yoga techniques (abdominal (adham) and complete (mahat)) breathing, each for 10 min in the supine position (i.e., savasana). For each condition, respiratory frequency, heart rate (HR), blood pressure and peripheral oxygen levels were collected. Results: The findings revealed that both abdominal and complete yoga breathing techniques promoted a decrease in respiratory frequency (p < 0.001, r = 0.61; p < 0.001, r = 0.61, respectively), and an increase in peripheral oxygen saturation (p < 0.001, r = 0.50; p < 0.001, r = 0.46, respectively), along with blood pressure decreases in all mean values, and a significant decrease in systolic pressure, considering all conditions (p = 0.034, W = 0.08). There were significant increases in standard deviation of HR during abdominal and complete yoga breathing techniques compared with the baseline (p = 0.003, r = 0.31; p < 0.001, r = 0.47, respectively), indicating enhanced parasympathetic activity. Moreover, the complete breathing technique exhibited the greatest variability in HRV measures, with several significant differences compared with abdominal breathing (standard deviation of HR, p < 0.001, r = 0.42; SD2, standard deviation of points perpendicular to the Poincaré parallel line, p < 0.003, r = 0.31; SD1/SD2, p < 0.003, r = 0.31), suggesting a more profound impact on autonomic modulation. Conclusions: simple, inexpensive and non-intrusive abdominal and complete yoga breathing techniques can effectively and momentarily enhance HRV and oxygen saturation in adults, mature adults and the elderly.

Mindfulness-Based Intervention Effect on the Psychophysiological Marker of Self-Regulation in Women With Endometriosis-Related Chronic Pain

Endometriosis is a gynecological disease that involves a broad biopsychosocial compromise with the potential to create a negative vicious cycle. Despite the complexity of factors influencing women's improvement, most interventions investigated target just the peripheral nociceptive sources of endometriosis-related pain. An alternative is intervening in self-regulation, which can potentially influence multiple domains of the illness experience. The present study examines the effect of a brief Mindfulness-Based Intervention (bMBI) on attention and autonomic nervous system regulation in women with endometriosis-related pain. Also, explore the interaction between these self-regulation domains and the affective pain dimension. An exploratory analysis of the secondary outcomes of a pilot randomized controlled trial was performed. The vagally-mediated Heart Rate Variability (vmHRV) at rest, cognitive stress, and recovery was employed to measure autonomic regulation. The Flanker and Stroop tasks were used to estimate the attention domains. Results showed that bMBI (n = 26) significantly improved Flanker accuracy and Flanker and Stroop reaction time compared to the control group (n = 28). bMBI significantly increased vmHRV at rest and recovery after cognitive stress. Attention mediated the bMBI effect on affective pain improvement. Results suggest that bMBI improves self-regulation domains with the potential to develop a broad biopsychosocial benefit in the endometriosis context. PERSPECTIVE: This article demonstrates the positive impact of a brief Mindfulness-Based Intervention on attention and parasympathetic regulation in women suffering from endometriosis-related pain. This mindfulness-induced self-regulation improvement can benefit affective pain and potentially multiple psychophysiological processes relevant to endometriosis.

Stress-induced brain responses are associated with BMI in women

Overweight and obesity are associated with altered stress reactivity and increased inflammation. However, it is not known whether stress-induced changes in brain function scale with BMI and if such associations are driven by peripheral cytokines. Here, we investigate multimodal stress responses in a large transdiagnostic sample using predictive modeling based on spatio-temporal profiles of stress-induced changes in activation and functional connectivity. BMI is associated with increased brain responses as well as greater negative affect after stress and individual response profiles are associated with BMI in females (pperm < 0.001), but not males. Although stress-induced changes reflecting BMI are associated with baseline cortisol, there is no robust association with peripheral cytokines. To conclude, alterations in body weight and energy metabolism might scale acute brain responses to stress more strongly in females compared to males, echoing observational studies. Our findings highlight sex-dependent associations of stress with differences in endocrine markers, largely independent of peripheral inflammation.

Dynamic regulatory processes among child welfare parents: Temporal associations between physiology and parenting behavior

This study examined how temporal associations between parents' physiological and behavioral responses may reflect underlying regulatory difficulties in at-risk parenting. Time-series data of cardiac indices (second-by-second estimates of inter-beat intervals - IBI, and respiratory sinus arrhythmia - RSA) and parenting behaviors were obtained from 204 child welfare-involved parents (88% mothers, Mage = 32.32 years) during child-led play with their 3- to 7-year-old children (45.1% female; Mage = 4.76 years). Known risk factors for maltreatment, including parents' negative social cognitions, mental health symptoms, and inhibitory control problems, were examined as moderators of intra-individual physiology-behavior associations. Results of ordinary differential equations suggested increases in parents' cardiac arousal at moments when they showed positive parenting behaviors. In turn, higher arousal was associated with momentary decreases in both positive and negative parenting behaviors. Individual differences in these dynamic processes were identified in association with parental risk factors. In contrast, no sample-wide RSA-behavior associations were evident, but a pattern of increased positive parenting at moments of parasympathetic withdrawal emerged among parents showing more total positive parenting behaviors. This study illustrated an innovative and ecologically-valid approach to examining regulatory patterns that may shape parenting in real-time and identified mechanisms that should be addressed in interventions.

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.

Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications

Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. Work from our group and others has demonstrated that data-analytic techniques can identify quantitative physiologic changes that precede clinical detection of meaningful events, and therefore may provide an important window for time-sensitive therapies. Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.

Wavelet Analysis and Self-Similarity of Photoplethysmography Signals for HRV Estimation and Quality Assessment

Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person's movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals' self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.

Concordance of mother-child respiratory sinus arrythmia is continually moderated by dynamic changes in emotional content of film stimuli

Evidence suggests that concordance between parent and child physiological states is an important marker of interpersonal interaction. However, studies have focused on individual differences in concordance, and we have limited understanding of how physiological concordance may vary dynamically based on the situational context. We examined whether mother-child physiological concordance was moderated by dynamic changes in emotional content of a film clip they viewed together. Second-by-second estimates of respiratory sinus arrythmia were obtained from mothers and children (N = 158, M_{child age} = 45.16 months) as they viewed a chase scene from a children's film. In addition, the film clip's negative emotional content was rated second-by-second. Results showed that mother-child dyads displayed positive physiological concordance only in seconds when there was an increase in the clip's negative emotional content. Thus, dynamic changes in mother-child physiological concordance may indicate dyadic responses to challenge.

HRVCam: robust camera-based measurement of heart rate variability

SIGNIFICANCE

Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts.

AIM

We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals.

APPROACH

HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals.

RESULTS

We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion.

CONCLUSION

HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.

Psychosocial stress reactivity habituates following acute physiological stress

Acute and chronic stress are important factors in the development of mental disorders. Reliable measurement of stress reactivity is therefore pivotal. Critically, experimental induction of stress often involves multiple “hits” and it is an open question whether individual differences in responses to an earlier stressor lead to habituation, sensitization, or simple additive effects on following events. Here, we investigated the effect of the individual cortisol response to intravenous catheter placement (IVP) on subsequent neural, psychological, endocrine, and autonomous stress reactivity. We used an established psychosocial stress paradigm to measure the acute stress response (Stress) and recovery (PostStress) in 65 participants. Higher IVP‐induced cortisol responses were associated with lower pulse rate increases during stress recovery (b = −4.8 bpm, p = .0008) and lower increases in negative affect after the task (b = −4.2, p = .040). While the cortisol response to IVP was not associated with subsequent specific stress‐induced neural activation patterns, the similarity of brain responses Pre‐ and PostStress was higher IVP‐cortisol responders (t[64] = 2.35, p = .022) indicating faster recovery. In conclusion, preparatory stress induced by IVP reduced reactivity in a subsequent stress task by modulating the latency of stress recovery. Thus, an individually stronger preceding release of cortisol may attenuate a second physiological response and perceived stress suggesting that relative changes, not absolute levels are crucial for stress attribution. Our study highlights that considering the entire trajectory of stress induction during an experiment is important to develop reliable individual biomarkers.

Dynamic behavior of the locus coeruleus during arousal-related memory processing in a multi-modal 7T fMRI paradigm

A body of animal and human evidence points to the norepinephrine (NE) locus coeruleus (LC) system in modulating memory for arousing experiences, but whether the LC would recast its role along memory stages remains unknown. Sedation precluded examination of LC dynamics during memory processing in animals. Here, we addressed the contribution of the LC during arousal-associated memory processing through a unique combination of dedicated ultra-high-field LC-imaging methods, a well-established emotional memory task, online physiological and saliva alpha-amylase measurements in young adults. Arousal-related LC activation followed amygdala engagement during encoding. During consolidation and recollection, activation transitioned to hippocampal involvement, reflecting learning and model updating. NE-LC activation is dynamic, plays an arousal-controlling role, and is not sufficient but requires interactions with the amygdala to form adaptive memories of emotional experiences. These findings have implications for understanding contributions of LC dysregulation to disruptions in emotional memory formation, observed in psychiatric and neurocognitive disorders.

Smartphone PPG: signal processing, quality assessment, and impact on HRV parameters

Photoplethysmography (PPG) is a simple optical technique used to detect blood volume changes in the micro-vascular bed of tissue in order to track the heartbeat. Smart-phone PPG, performed with the phones camera, has became popular in recent years due to a boom in digital health apps that help people monitor their health parameters. However, many apps struggle with getting readings that are accurate enough to estimate heart rate variability (HRV) one of the most popular biomarkers in the preventive health space. The main obstacle is the multitude of factors that impact PPG results: unique technical characteristics of different smartphone models, frames per second (FPS) rate and the way color is recoarded, brightness and ambient flash levels, finger placement, in-measurement movement, etc. These factors may decrease the accuracy of the signal extracted from the camera's video stream and produce additional errors in the computation of HRV parameters. Thus, there is a need to estimate signal quality and predict possible bias in HRV parameter calculation. In this paper, we describe the method for processing signal from smartphone cameras, estimating signal quality, recognizing RR intervals, and predicting bias of simple HRV parameters.

Feasibility of assessing ultra-short-term pulse rate variability from video recordings

Objectives

Remote photoplethysmography (rPPG) is a promising non-contact measurement technique for assessing numerous physiological parameters: pulse rate, pulse rate variability (PRV), respiratory rate, pulse wave velocity, blood saturation, blood pressure, etc. To justify its use in ultra-short-term (UST) PRV analysis, which is of great benefit for several healthcare applications, the agreement between rPPG- and PPG-derived UST-PRV metrics was studied.

Approach

Three time-domain metrics—standard deviation of normal-to-normal (NN) intervals (SDNN), root mean square of successive NN interval differences (RMSSD), and the percentage of adjacent NN intervals that differ from each other by more than 50 ms (pNN50)—were extracted from 56 video recordings in a publicly available data set. The selected metrics were calculated on the basis of three groups of 10 s recordings and their average, two groups of 30 s recordings and their average, and a group of 60 s recordings taken from the full-length recordings and then compared with metrics derived from the corresponding reference (PPG) pulse waveform signals by using correlation and effect size parameters, and Bland–Altman plots.

Main results

The results show there is stronger agreement as the recording length increases for SDNN and RMSSD, yet there is no significant change for pNN50. The agreement parameters reach r = 0.841 (p < 0.001), r = 0.529 (p < 0.001), and r = 0.657 (p < 0.001), estimated median bias −1.52, −2.28 ms and −1.95% and a small effect size for SDNN, RMSSD, and pNN50 derived from the 60 s recordings, respectively.

Significance

Remote photoplethysmography-derived UST-PRV metrics manage to capture UST-PRV metrics derived from reference (PPG) recordings well. This feature is highly desirable in numerous applications for the assessment of one’s health and well-being. In future research, the validity of rPPG-derived UST-PRV metrics compared to the gold standard electrocardiography recordings is to be assessed.

Evidence Based Recommendations for Designing Heart Rate Variability Studies

Heart rate variability (HRV) analysis is a powerful instrument that provides information about the heart conditions. However, there exist some limitations in the use of HRV in the clinical practice. Examples are the lack of reference values for healthy populations, different HR (Heart Rate) acquisition systems, and varying software packages. Other factors that affect HRV values are the influence of lifestyle, drugs and alcohol consumption, and pollution. In this work, recommendations to perform HRV-based experiments were established. These suggestions refer to best moment of the day to record data, the optimal body position, and the quality and duration of the recorded signals. In this way, HR data from 6 healthy subjects (2 women, 4 men), with median age of 50 years old, were recorded during 15 days, 3 times a day. Recordings were performed in the following situations: both supine and sitting body positions, in the morning, in the afternoon and at night. Data were processed and HRV analysis was performed. Distorting factors affecting HRV have been determined. The most stable HRV indexes (less variation over the days) have also been established. For this task, a variation coefficient was calculated for each parameter, as the ratio between the standard deviation and the mean value. Results indicated that HR data should be recorded in the morning, the sitting position. Related to signals duration, when comparing HR signals, they should be of equal length (same recording time). In addition, HRVi (HRV triangular index) and MADRR (median of the absolute differences between adjacent RR intervals) resulted in the most robust indexes in both low and high frequency domains. For global indexes, the ApEn (approximate entropy) measure emerged as the most stable one. As a conclusion, researchers must be extremely cautious in studies involving HRV analysis; the moment of the day to record data, the body position, or the quality of recorded data will produce different HR signals, and thus, the values of the HRV parameters will be different in each case. This may clearly bias the conclusions of the study.

Heart Rate and Heart Rate Variability From Single-Channel Video and ICA Integration of Multiple Signals

Unobtrusive monitoring of vital signs is relevant for both medical (patient monitoring) and non-medical applications (e.g., stress and fatigue monitoring). In this paper, we focus on the use of imaging photoplethysmography (iPPG). High frame rate videos were acquired by using a monochrome camera and an optical band-pass filter ([Formula: see text] nm). To enhance iPPG signal, we investigated the use of independent component analysis (ICA) pre-processing applied to iPPG signal from different regions of the face. Methodology was tested on [Formula: see text] healthy volunteers. Heart rate (HR) and standard time and frequency domain descriptors of heart rate variability (HRV), simultaneously extracted from videos and ECG data, were compared. A mean absolute error (MAE) about 3.812 ms was observed for normal-to-normal intervals with or without ICA pre-processing. Smaller MAE values of frequency domain descriptors were observed when ICA pre-processing was used. The impact of both video frame rate and video signal interval were also analyzed. All the results support the conclusion that proposed ICA pre-processing can effectively improve the HR and HRV assessment from iPPG.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Background and purpose

With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.

Methods

In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.

Results

As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.

Conclusion

The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.

Short-term association between personal exposure to noise and heart rate variability: The RECORD MultiSensor Study

BACKGROUND

Studies revealed long-term associations between noise exposure and cardiovascular health, but the underlying short-term mechanisms remain uncertain.

OBJECTIVES

To explore the concomitant and lagged short-term associations between personal exposure to noise and heart rate variability (HRV) in a real life setting in the Île-de-France region.

METHODS

The RECORD MultiSensor Study collected between July 2014 and June 2015 noise and heart rate data for 75 participants, aged 34-74 years, in their living environments for 7 days using a personal dosimeter and electrocardiography (ECG) sensor on the chest. HRV parameters and noise levels were calculated for 5-min windows. Short-term relationships between noise level and log-transformed HRV parameters were assessed using mixed effects models with a random intercept for participants and a temporal autocorrelation structure, adjusted for heart rate, physical activity (accelerometry), and short-term trends.

RESULTS

An increase by one dB(A) of A-weighted equivalent sound pressure level (Leq) was associated with a 0.97% concomitant increase of the Standard deviation of normal to normal intervals (SDNN) (95% CI: 0.92, 1.02), of 2.08% of the Low frequency band power (LF) (95% CI: 1.97, 2.18), of 1.30% of the High frequency band power (HF) (95% CI: 1.17, 1.43), and of 1.16% of the LF/HF ratio (95% CI: 1.10, 1.23). The analysis of lagged exposures to noise adjusted for the concomitant exposure illustrates the dynamic of recovery of the autonomic nervous system. Non-linear associations were documented with all HRV parameters with the exception of HF. Piecewise regression revealed that the association was almost 6 times stronger below than above 65 Leq dB(A) for the SDNN and LF/HF ratio.

CONCLUSION

Personal noise exposure was found to be related to a concomitant increase of the overall HRV, with evidence of imbalance of the autonomic nervous system towards sympathetic activity, a pathway to increased cardiovascular morbidity and mortality.

Influence of visual elicitation over emotion regulation: An investigation employing the heart rate variability

Several works studied the elicitation of emotions through the exposure of individuals to relevant stimuli, using spectral analysis of Heart Rate Variability (HRV) when people are subject to emotional elicitation. If correlation exists between HRV and emotional responses, spectral analysis can be used to study emotion regulation under external stimuli. In this work, we studied the relationship between visual elicitation and emotion regulation, employing HRV. Images (with pleasant, unpleasant and neutral emotional content) were selected from the IAPS (International Affective Picture System) dataset. Ninety-eight participants were enrolled, and subject to view all images, displayed in random order for each participant. Heart rate was recorded during the experiment, and HRV analysis was performed. Spectral values were studied for the different images. The presentation order of images was relevant, mainly when unpleasant images were viewed in first place; this significantly affects HRV values. Spectral values were higher for men, being this difference stronger when pleasant pictures were displayed. Age and gender dependences of spectral indexes were found. The influence of visual elicitation, with different emotional contents, over HRV, was assessed. Results indicate that HRV parameters are affected when individuals are subject to external, emotional-based stimuli.

Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm

OBJECTIVE

Major depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and the autonomic nervous system. This study applied a machine-learning method to classify MDD and control groups by incorporating data from serum proteomic analysis and heart rate variability (HRV) analysis for the identification of novel peripheral biomarkers.

METHODS

The study subjects consisted of 25 drug-free female MDD patients and 25 age- and sex-matched healthy controls. First, quantitative serum proteome profiles were analyzed by liquid chromatography-tandem mass spectrometry using pooled serum samples from 10 patients and 10 controls. Next, candidate proteins were quantified with multiple reaction monitoring (MRM) in 50 subjects. We also analyzed 22 linear and nonlinear HRV parameters in 50 subjects. Finally, we identified a combined biomarker panel consisting of proteins and HRV indexes using a support vector machine with recursive feature elimination.

RESULTS

A separation between MDD and control groups was achieved using five parameters (apolipoprotein B, group-specific component, ceruloplasmin, RMSSD, and SampEn) at 80.1% classification accuracy. A combination of HRV and proteomic data achieved better classification accuracy.

CONCLUSIONS

A high classification accuracy can be achieved by combining multimodal information from heart rate dynamics and serum proteomics in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for MDD diagnosis.

Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method

The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments. The performance of the proposed method in delineating the dynamics of momentary HRV measurement was evaluated with four commonly used time courses of HRV measures on both synthetic time series and real ECG recordings from human subjects and dogs. Our results showed that a smaller time increment could capture more dynamical information on transient changes. Considering a too short increment such as 10 s would cause the indented time courses of the four measures, a 1-min time increment (4-min overlapping) was suggested in the analysis of mHRV in the study. ECG recordings from human subjects and dogs were used to further assess the effectiveness of the proposed method. The pilot study demonstrated that the proposed analysis of mHRV could provide more accurate assessment of the dynamical changes in cardiac activity than the conventional measures of HRV (without time overlapping). The proposed method may provide an efficient means in delineating the dynamics of momentary HRV and it would be worthy performing more investigations.

Ameliorating Effect of Transcutaneous Electroacupuncture on Impaired Gastric Accommodation in Patients with Postprandial Distress Syndrome-Predominant Functional Dyspepsia: A Pilot Study

Patients with functional dyspepsia (FD) have both reduced gastric accommodation and impaired gastric motility that are difficult to treat. The aim of this study was to investigate the therapeutic potential of transcutaneous electroacupuncture (TEA) for both of these disorders in FD patients. Acute experiments were performed in FD patients to study the effect of TEA and sham-TEA on gastric accommodation assessed by a nutrient drink test and gastric motility assessed by the measurement of the electrogastrogram (EGG). TEA or sham-TEA was performed via cutaneous electrodes at acupoints ST36 and PC6 or sham-points nonacupoints. It was found that (1) gastric accommodation (maximum tolerable volume) was reduced in FD patients compared with the controls (P < 0.03). TEA improved gastric accommodation in FD patients (P < 0.02). (2) Acute TEA significantly increased the percentage and power of normal gastric slow waves in the fed state assessed in the FD patients by the EGG in comparison with sham-TEA. (3) TEA increased vagal activity assessed by the spectral analysis of the heart rate variability in the fed state in FD patients. It was concluded that needleless method of transcutaneous electroacupuncture may have a therapeutic potential for treating both impaired gastric accommodation and impaired gastric motility in patients with FD.

gHRV: Heart rate variability analysis made easy

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results. This paper also contains, as an example of use, a clinical analysis performed with the gHRV tool, namely to determine whether the heart rate variability indexes change across different stages of sleep. Results from tests completed by researchers who have tried gHRV are also explained: in general the application was positively valued and results reflect a high level of satisfaction. gHRV is in continuous development and new versions will include suggestions made by testers.

Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera

We present a new method for measuring photoplethysmogram signals remotely using ambient light and a digital camera that allows for accurate recovery of the waveform morphology (from a distance of 3 m). In particular, we show that the peak-to-peak time between the systolic peak and diastolic peak/inflection can be automatically recovered using the second-order derivative of the remotely measured waveform. We compare measurements from the face with those captured using a contact fingertip sensor and show high agreement in peak and interval timings. Furthermore, we show that results can be significantly improved using orange, green, and cyan color channels compared to the tradition red, green, and blue channel combination. The absolute error in interbeat intervals was 26 ms and the absolute error in mean systolic-diastolic peak-to-peak times was 12 ms. The mean systolic-diastolic peak-to-peak times measured using the contact sensor and the camera were highly correlated, ρ = 0.94 (p 0.001). The results were obtained with a camera frame-rate of only 30 Hz. This technology has significant potential for advancing healthcare.

Improvements in remote cardiopulmonary measurement using a five band digital camera

Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing. However, traditional RGB cameras have limited frequency resolution. We present results of PPG measurements from a novel five band camera and show that alternate frequency bands, in particular an orange band, allowed physiological measurements much more highly correlated with an FDA approved contact PPG sensor. In a study with participants (n = 10) at rest and under stress, correlations of over 0.92 (p 0.01) were obtained for heart rate, breathing rate, and heart rate variability measurements. In addition, the remotely measured heart rate variability spectrograms closely matched those from the contact approach. The best results were obtained using a combination of cyan, green, and orange (CGO) bands; incorporating red and blue channel observations did not improve performance. In short, RGB is not optimal for this problem: CGO is better. Incorporating alternative color channel sensors should not increase the cost of such cameras dramatically.

Heart rate variability dynamics during low-dose propofol and dexmedetomidine anesthesia

Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

An open source tool for heart rate variability spectral analysis

In this paper we describe a software package for developing heart rate variability analysis. This package, called RHRV, is a third party extension for the open source statistical environment R, and can be freely downloaded from the R-CRAN repository. We review the state of the art of software related to the analysis of heart rate variability (HRV). Based upon this review, we motivate the development of an open source software platform which can be used for developing new algorithms for studying HRV or for performing clinical experiments. In particular, we show how the RHRV package greatly simplifies and accelerates the work of the computer scientist or medical specialist in the HRV field. We illustrate the utility of our package with practical examples.

Wavelet leader based multifractal analysis of heart rate variability during myocardial ischaemia

Heart rate variability is a non invasive and indirect measure of the autonomic control of the heart. Therefore, alterations to this control system caused by myocardial ischaemia are reflected in changes in the complex and irregular fluctuations of this signal. Multifractal analysis is a well suited tool for the analysis of this kind of fluctuations, since it gives a description of the singular behavior of a signal. Recently, a new approach for multifractal analysis was proposed, the wavelet leader based multifractal formalism, which shows remarkable improvements over previous methods. In order to characterize and detect ischaemic episodes, in this work we propose to perform a short-time windowed wavelet leader based multifractal analysis. Our results suggest that this new method provides appropriate indexes that could be used as a tool for the detection of myocardial ischaemia.

Advancements in noncontact, multiparameter physiological measurements using a webcam

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.

The effect of a meal and water loading on heart rate variability in children with functional dyspepsia

BACKGROUND

Alteration in autonomic function has been implicated as a possible pathophysiologic mechanism in functional dyspepsia (FD) in adults.

METHODS

This was a two-part study. For the first part of the study, nine children with FD and 28 controls underwent heart rate variability (HRV) analysis for 30 min baseline and for 60 min following a test meal. For the second part of the study, eight children with FD and 26 controls underwent HRV analysis for 30 min baseline and for 60 min following rapid water loading. HRV analysis yielded measures for power in the high frequency (HF) band (vagal activity), power in the low frequency (LF) band (mainly sympathetic activity), and LF/HF (sympathovagal balance).

RESULTS

HRV parameters did not differ between FD patients and controls in either baseline period or following water loading. Following the meal, HF was higher and LF and LF/HF were lower in FD patients as compared to controls. Baseline LF/HF was positively correlated with the water load volume in controls and negatively correlated with the water load volume in FD patients.

CONCLUSION

FD in children is associated with an abnormal autonomic response best demonstrated following a meal. The relationship between baseline sympathovagal balance and water load volume in FD patients is distinctly different from the relationship in control children.

Detecting sleep apnea by heart rate variability analysis: assessing the validity of databases and algorithms

Obstructive sleep apnea (OSA) is a serious disorder caused by intermittent airway obstruction which may have dangerous impact on daily living activities. Heart rate variability (HRV) analysis could be used for diagnosing OSA, since this disease affects HRV during sleep. In order to validate different algorithms developed for detecting OSA employing HRV analysis, several public or proprietary data collections have been employed for different research groups. However, for validation purposes, it is obvious and evident the lack of a common standard database, worldwide recognized and accepted by the scientific community. In this paper, different algorithms employing HRV analysis were applied over diverse public and proprietary databases for detecting OSA, and the outcomes were validated in terms of a statistical analysis. Results indicate that the use of a specific database may strongly affect the performance of the algorithms, due to differences in methodologies of processing. Our results suggest that researchers must strongly take into consideration the database used when quoting their results, since selected cases are highly database dependent and would bias conclusions.

Transcutaneous electroacupuncture improves dyspeptic symptoms and increases high frequency heart rate variability in patients with functional dyspepsia

The aim of the study was to evaluate the therapeutic value and possible mechanisms of transcutaneous electroacupuncture (TEA) in a double-blind and cross-over study in patients with functional dyspepsia (FD). Twenty-seven patients with FD were enrolled and the study consisted of two parts: (i) acute effects of TEA at PC6 and ST36 on gastric slow waves and heart rate variability and (ii) chronic (2 weeks) effects of TEA at PC6 and ST36 on dyspepsia symptoms, gastric slow waves, heart rate variability and neuropeptide Y (NPY) and motilin. The results of this study are: (i) The dyspepsia symptom score was decreased by 55% at the end of chronic TEA and the improvement was significant (P < 0.01); (ii) the high frequency (HF) assessed from the spectral analysis of heart rate variability was markedly increased with both acute TEA (76% increase, P = 0.01) and chronic TEA (75% increase, P = 0.025); (iii) gastric slow waves were not altered by either acute or chronic TEA; and (iv) the plasma level of NPY but not motilin was increased after chronic TEA. Non-invasive and needleless transcutaenous electroacupuncture at ST36 and PC6 markedly improves dyspepsia symptoms and the improvement may be associated with the increase in HF heart rate variability and the modulation of NPY.

Time-varying analysis of heart rate variability with kalman smoother algorithm

A time-varying parametric spectrum estimation method for analyzing nonstationary heart rate variability signals is presented. In the method, the nonstationary signal is first modeled with time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The spectrum estimates for each time are then obtained from the estimated model parameters. Statistics of the obtained spectrum estimates are derived using the error propagation principle. The obtained spectrum estimates can further be decomposed into separate components and, thus, the time-variation of low and high frequency components of heart rate variability can be examined separately.

Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm

A time-varying parametric spectrum estimation method for analysing non-stationary heart rate variability signals is presented. As a case study, the dynamics of heart rate variability during an orthostatic test is examined. In this method, the non-stationary signal is first modelled with a time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The benefit of using the Kalman smoother is that the lag error present in a Kalman filter, as well as in all other adaptive filters, can be avoided. The spectrum estimates for each time instant are then obtained from the estimated model parameters. Statistics of the obtained spectrum estimates are derived using the error propagation principle. The obtained spectrum estimates can further be decomposed into separate components and, thus, the time variation of low- and high-frequency components of heart rate variability can be examined separately. By using the presented method, high resolution time-varying spectrum estimates with no lag error can be produced. Other benefits of the method are the straightforward procedure for evaluating the statistics of the spectrum estimates and the option of spectral decomposition.

Forecasting epilepsy from the heart rate signal

Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30 s prior to seizure onset Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.

Tracking rhythmicity in nonstationary quasi-periodic biomedical signals using adaptive time-varying covariance

A time-varying covariance method for detecting and quantifying the evolution of rhythmicity (frequency) in persistently varying quasi-periodic nonstationary signals is presented. The basic method, evaluated using chirp signals, utilizes a shifting window of fixed length. A substantial reduction in estimation bias and variability are obtained by utilizing an adaptive window whose length is dependent on past frequency estimates. The adaptive window yields estimates that are comparable in accuracy to those obtained using high-resolution time-frequency representation but with lower computation requirements and the potential for on-line application. Finally, an example of the application of the method for analyzing a neural recording is also illustrated.

Electroacupuncture accelerates gastric emptying in association with changes in vagal activity

Gastroparesis is a disorder with a lack of treatment options and this study investigated the effect of electroacupuncture on gastric emptying and involved mechanisms. Dogs implanted with a duodenal cannula and serosal electrodes were fed with Ensure mixed with phenol red, and the gastric effluent was collected. Electroacupuncture was performed from 30 min before until 45 min after the meal. Gastric myoelectrical activity and electrocardiogram were recorded. Gastric emptying was significantly improved with electroacupuncture. Vagal activity assessed from the spectral analysis of heart rate variability was markedly increased with electroacupuncture. Electroacupuncture increased the regularity of gastric slow waves in both the proximal and distal stomach. It also increased the number of spike bursts in the distal but not proximal stomach. Electroacupuncture accelerates gastric emptying of liquid in dogs and its potential for treating gastroparesis may be explored. The effect may be attributed to improvement in gastric slow-wave rhythmicity and antral contractile (spike) activity and may possibly involve the vagal pathway.

Wavelet transform analysis of heart rate variability during myocardial ischaemia

Analysis of heart rate variability (HRV) is a valuable, non-invasive method for quantifying autonomic cardiac control in humans. Frequency-domain analysis of HRV involving myocardial ischaemic episodes should take into account its non-stationary behaviour. The wavelet transform is an alternative tool for the analysis of non-stationary signals. Fourteen patients have been analysed, ranging from 40 to 64 years old and selected from the European Electrocardiographic ST-T Database (ESDB). These records contain 33 ST episodes, according to the notation of the ESDB, with durations of between 40s and 12 min. A method for analysing HRV signals using the wavelet transform was applied to obtain a time-scale representation for very low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands using the orthogonal multiresolution pyramidal algorithm. The design and implementation using fast algorithms included a specially adapted decomposition quadrature mirror filter bank for the frequency bands of interest. Comparing a normality zone against the ischaemic episode in the same record, increases in LF (0.0112 +/- 0.0101 against 0.0175 +/- 0.0208 s2 Hz(-1); p<0.1) and HF (0.0011 +/- 0.0008 against 0.00 17 +/- 0.0020 s2 Hz(-1); p<0.05) were obtained. The possibility of using these indexes to develop an ischaemic-episode classifier was also tested. Results suggest that wavelet analysis provides useful information for the assessment of dynamic changes and patterns of HRV during myocardial ischaemia.

Fuzzy logic in a patient supervision system

A patient supervision system in progress for intensive and coronary care units, focused on patients with acute myocardial infarct is briefly described particularly regarding the role that fuzzy logic is playing in its design, and why this is so.

Automatic ectopic beat elimination in short-term heart rate variability measurement

Our studies deal with fully automatic measurement of heart rate variability (HRV) in short term electrocardiograms. Presently, all existing HRV analysis programs require user intervention for ectopic beat identification, especially of supraventricular ectopic beats (SVE). This makes the HRV measurement in large, e.g. epidemiological studies problematic. In this paper, we present a fully automatic algorithm for the discrimination of the ventricular (VE) and SVE ectopic beats from the normal QRS complexes suited for a reliable HRV analysis. The QRS identification is based on the template matching method. The ectopic beats are identified based on several morphological and timing properties of the electrocardiogram (ECG) signal. The method incorporates several approaches and makes HRV analysis of large numbers of electrocardiograms feasible. It uses the template matching for the basic morphology check of the QRS complex and the P-wave, the timing information to avoid unnecessary ectopic beat checks and to adjust thresholds and it also looks for a special QRS morphology, which is common in VEs. We used a testing set of 69 electrocardiograms selected from a large number of recordings. The selected ECGs contained abnormalities including ectopic beats, right branch bundle block, respiratory arrhythmia, blocked atrial extrasystole, high amplitude and wide T-waves. The evaluation of our method showed a specificity of 0.99, supraventricular ectopic beat sensitivity of 0.99 and ventricular ectopic beat sensitivity of 0.98.

Alterations of autonomic nervous activity in recurrence of variant angina

OBJECTIVE

To investigate whether autonomic nervous activity is involved in the recurrence of spontaneous coronary spasm in variant angina.

DESIGN

Retrospective analysis.

SETTING

Cardiology department of a university hospital.

PATIENTS

18 patients with variant angina were divided into single attack group (SA; nine patients) and multiple attack group (MA; nine patients) according to the frequency of ischaemic episodes with ST segment elevation during 24 hour Holter monitoring.

METHODS

Heart rate variability indices were calculated using MemCalc method, which is a combination of the maximum entropy method for spectral analysis and the non-linear least squares method for fitting analysis, at 30 second intervals for 30 second periods, from 40 minutes before the attack to 30 minutes after the attack. High frequency (HF; 0.04-0.15 Hz) was defined as a marker of parasympathetic activity, and the ratio of low frequency (LF; 0.15-0.40 Hz) to high frequency (LF/HF) as an indicator of sympathetic activity. The averaged value during the 40 to 30 minute period before an attack was defined as the baseline.

RESULTS

Compared with baseline, the HF component decreased in both groups at two minutes before the attack (p < 0.01), and the LF/HF ratio decreased at three minutes before the attack (p < 0.01). The baseline LF/HF was lower in the MA group than in the SA group (p < 0. 01).

CONCLUSIONS

A reduction of sympathetic activity may play a key role in determining the recurrence of transient ischaemic events caused by spontaneous coronary spasm in patients with variant angina.