Separation of respiratory influences from the tachogram: a methodological evaluation

Cardiorespiratory disturbances in focal impaired awareness seizures: Insights from wearable ECG monitoring

PURPOSE

Seizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA).

METHODS

We extracted ECG signals across three time points - five minutes baseline and preictal, ten minutes postictal - and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means.

RESULTS

The mean preictal heart rate was 69.95 bpm (95 % CI 65.6 - 74.3), and it increased to 82 bpm, 95 % CI (77.51 - 86.47) and 84.11 bpm, 95 % CI (76.9 - 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 - 17.1), 14.8 (95 % CI 13.4 - 16.2) and 15.1 (95 % CI 14 - 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures.

CONCLUSIONS

We found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.

Multimodal decoding of error processing in a virtual reality flight simulation

Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multimodal physiological data can be used to decode error processing, which has been studied, to date, with brain signals only. We examined the feasibility of decoding errors solely with pupil size data and proposed a hybrid decoding approach combining electroencephalographic (EEG) and pupillometric signals. Moreover, we analyzed if hybrid approaches can improve existing EEG-based classification approaches and focused on setups that offer increased usability for practical applications, such as the presented game-like virtual reality flight simulation. Our results indicate that classifiers trained with pupil size data can decode errors above chance. Moreover, hybrid approaches yielded improved performance compared to EEG-based decoders in setups with a reduced number of channels, which is crucial for many out-of-the-lab scenarios. These findings contribute to the development of hybrid brain-computer interfaces, particularly in combination with wearable devices, which allow for easy acquisition of additional physiological data.

Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component

This study's primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, 28 volunteers were placed in a dry hyperbaric chamber, where they experienced varying pressures from 1 to 5 atmospheres, with five sequential stops lasting five minutes each at different atmospheric pressures. The HRV was dissected into two components: the respiratory component, which is linked to respiration; and the residual component, which is influenced by factors beyond respiration. Nine parameters were assessed, including the respiratory rate, four classic HRV temporal parameters, and four frequency parameters. A k-nearest neighbors classifier based on cosine distance successfully identified the atmospheric pressures to which the subjects were exposed to. The classifier achieved an 88.5% accuracy rate in distinguishing between the 5 atm and 3 atm stages using only four features: respiratory rate, heart rate, and two frequency parameters associated with the subjects' sympathetic responses. Furthermore, the study identified 6 out of 28 subjects as having atypical responses across all pressure levels when compared to the majority. Interestingly, two of these subjects stood out in terms of gender and having less prior diving experience, but they still exhibited normal responses to immersion. This suggests the potential for establishing distinct safety protocols for divers based on their previous experience and gender.

Enhancing Safety in Hyperbaric Environments through Analysis of Autonomic Nervous System Responses: A Comparison of Dry and Humid Conditions

Diving can have significant cardiovascular effects on the human body and increase the risk of developing cardiac health issues. This study aimed to investigate the autonomic nervous system (ANS) responses of healthy individuals during simulated dives in hyperbaric chambers and explore the effects of the humid environment on these responses. Electrocardiographic- and heart-rate-variability (HRV)-derived indices were analyzed, and their statistical ranges were compared at different depths during simulated immersions under dry and humid conditions. The results showed that humidity significantly affected the ANS responses of the subjects, leading to reduced parasympathetic activity and increased sympathetic dominance. The power of the high-frequency band of the HRV after removing the influence of respiration, PHF⟂¯, and the number of pairs of successive normal-to-normal intervals that differ by more than 50 ms divided by the total number of normal-to-normal intervals, pNN50¯, indices were found to be the most informative in distinguishing the ANS responses of subjects between the two datasets. Additionally, the statistical ranges of the HRV indices were calculated, and the classification of subjects as "normal" or "abnormal" was determined based on these ranges. The results showed that the ranges were effective at identifying abnormal ANS responses, indicating the potential use of these ranges as a reference for monitoring the activity of divers and avoiding future immersions if many indices are out of the normal ranges. The bagging method was also used to include some variability in the datasets' ranges, and the classification results showed that the ranges computed without proper bagging represent reality and its associated variability. Overall, this study provides valuable insights into the ANS responses of healthy individuals during simulated dives in hyperbaric chambers and the effects of humidity on these responses.

Slow breathing for reducing stress: The effect of extending exhale

INTRODUCTION

Slow breathing techniques are commonly used to reduce stress. While it is believed by mind-body practitioners that extending the exhale time relative to inhale increases relaxation, this has not been demonstrated.

METHODS

We conducted a 12-week randomized, single-blinded trial among 100 participants to compare if yoga-based slow breathing with an exhale greater inhale versus an exhale equals inhale produces measurable differences in physiological and psychological stress among healthy adults.

RESULTS

Participants mean individual instruction attendance was 10.7 ± 1.5 sessions out of 12 offered sessions. The mean weekly home practice was 4.8 ± 1.2 practices per week. There was no statistical difference between treatment groups for frequency of class attendance, home practice, or achieved slow breathing respiratory rate. Participants demonstrated fidelity to assigned breath ratios with home practice as measured by remote biometric assessments through smart garments (HEXOSKIN). Regular slow breathing practice for 12 weeks significantly reduced psychological stress as measured by PROMIS Anxiety (-4.85 S.D. ± 5.53, confidence interval [-5.60, -3.00], but not physiological stress as measured by heart rate variability. Group comparisons showed small effect size differences (d = 0.2) with further reductions in psychological stress and physiological stress from baseline to 12 weeks for exhale greater than inhale versus exhale equals inhale, however these differences were not statistically significant.

CONCLUSION

While slow breathing significantly reduces psychological stress, breath ratios do not have a significant differential effect on stress reduction among healthy adults.

Single-lead ECG based autonomic nervous system assessment for meditation monitoring

We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.

A subspace projection approach to quantify respiratory variations in the f-wave frequency trend

Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information.

Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients.

Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS).

Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p &lt; 0.03).

Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability

OBJECTIVES

an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed.

METHODS

ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response.

RESULTS

PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive.

CONCLUSIONS

PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results.

Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods

Objective.The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls.Approach.The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method.Main results.The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, -17.5%; univariate AR, -18.3%; bivariate AR, -23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing.Significance.Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

Effects of Respiratory Rate on Heart Rate Variability in Neurologic Outpatients with Epilepsies or Migraine: A Preliminary Study

BACKGROUND

Variation of spontaneous respiratory rates and influences of spontaneous and paced breathing rates on heart rate variability (HRV) were assessed in patients with epilepsy or migraine, and HRV parameters were compared between the groups.

MATERIALS AND METHODS

Thirty neurologic outpatients, 16 diagnosed with epilepsies and 14 with migraine, were included. Autonomic testing consisted of short-term HRV, the deep breathing test (DBT), and measurement of HRV at systematically changed breathing rates (paced breathing, 5-18 breaths per minute, bpm).

RESULTS

Spontaneous respiratory rate during short-term HRV varied from 9 to 23 bpm in the epileptic group and from 5 to 21 bpm in migraine patients and was significantly and negatively correlated with SD of all normal RR intervals (SDNN) and total power (TP) in epileptic patients but not in migraine patients. Paced breathing rate had a significant effect on all HRV parameters assessed in both groups. HRV (SD1, SDNN, TP) and DBT (E-I, SD1, SDNN) parameters were significantly lower in the epileptic group. Group differences were significantly greater during slow compared to fast breathing.

CONCLUSIONS

An important and new finding is the wide variation of spontaneous respiratory rate in both groups, along with the significant negative correlation with the assessed HRV parameters. The reduction of HRV during slow breathing in epileptic patients may indicate a diminished cardiorespiratory coupling caused by a probable loss of sensitivity within the cardiovagal brainstem circuitry.

Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia

We develop a technique for the multivariate data analysis of perturbed self-sustained oscillators. The approach is based on the reconstruction of the phase dynamics model from observations and on a subsequent exploration of this model. For the system, driven by several inputs, we suggest a dynamical disentanglement procedure, allowing us to reconstruct the variability of the system's output that is due to a particular observed input, or, alternatively, to reconstruct the variability which is caused by all the inputs except for the observed one. We focus on the application of the method to the vagal component of the heart rate variability caused by a respiratory influence. We develop an algorithm that extracts purely respiratory-related variability, using a respiratory trace and times of R-peaks in the electrocardiogram. The algorithm can be applied to other systems where the observed bivariate data can be represented as a point process and a slow continuous signal, e.g. for the analysis of neuronal spiking. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Increased Respiratory Modulation of Blood Pressure in Hypertensive Patients

Objective

Although the important role of respiratory modulation of the cardiovascular system in the development of hypertension has been demonstrated in animal studies, little research has assessed this modulation in essential hypertensive patients. We aimed to explore whether respiratory-related variations in cardiovascular variables are changed in hypertensive patients and their potential relationships with the respiratory pattern.

Methods

Respiration, ECG, and beat-to-beat blood pressure (BP) were simultaneously measured in 46 participants (24 hypertensive patients and 22 normotensive participants) during rest and a mental arithmetic task (MAT). Respiratory-triggered averaging and orthogonal subspace projection methods were used to assess the respiratory modulations of BP and heart rate (HR). Respiratory parameters including inspiratory time, expiratory time, respiratory rate and their variabilities were also characterized.

Results

The inspiratory time, expiratory time, respiratory rate and their variabilities were not different between hypertensive and normotensives. Additionally, the modulation of HR by respiration was also similar between the two groups. Hypertensive patients exhibited an amplified respiratory modulation of systolic BP (SBP), as assessed from the amplitude of respiratory-related changes and the percentage of the power of respiratory-related variation, and also reflected from the temporal pattern of respiratory modulation of SBP. The exaggerated respiratory-related variation of SBP in hypertensive patients accounted for ≈23% of the total power of SBP, producing an absolute change of ≈4.5 mmHg in SBP. MAT was characterized by decreased inspiratory time and increased variabilities of expiratory time and respiratory rate with no changes in the amplitude of respiratory modulations.

Conclusion

Hypertensive patients had excessive respiratory modulation of SBP, despite having similar respiratory pattern with normotensives. These findings highlight the importance of respiratory influence in BP variation and suggest that respiratory modulation of SBP may have prognostic information for cardiovascular events in hypertensive patients.

Unconstrained Estimation of HRV Indices After Removing Respiratory Influences From Heart Rate

OBJECTIVE

This paper proposes an approach to better estimate the sympathovagal balance (SB) and the respiratory sinus arrhythmia (RSA) after separating respiratory influences from the heart rate (HR).

METHODS

The separation is performed using orthogonal subspace projections and the approach is first tested using simulated HR and respiratory signals with different spectral properties. Then, RSA and SB are estimated during autonomic blockade and stress using the proposed approach and the classical heart rate variability (HRV) analysis. Both real- and ECG-derived respiration (EDR) are used and the reliability of the EDR is evaluated.

RESULTS

Mean absolute percentage errors lower than [Formula: see text] were obtained after removing previously known respiratory signals from simulated HR. The proposed indices were able to improve the quantification of SB during autonomic withdrawal. In the stress data, differences ( ) among relaxed and stressful phases were found with the proposed approach, using both the real respiration and the EDR, but they disappeared when using the classical HRV.

CONCLUSION

A better assessment of the autonomic nervous system' response to pharmacological blockade and stress can be achieved after removing respiratory influences from HR, and this can be done using either the real respiration or the EDR.

SIGNIFICANCE

This work can be used to better identify vagal withdrawal and increased sympathetic activation when the classical HRV analysis fails due to the respiratory influences on HR. Furthermore, it can be computed using only the ECG, which is an advantage when developing wearable systems with limited number of sensors.

Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG

Frequency-domain indices of heart rate variability (HRV) have been used as markers of sympathovagal balance. However, they have been shown to be degraded by interindividual or task-dependent variability, and especially variations in breathing frequency. The study introduces a method to analyze respiration-(vagally) mediated HRV, to better assess subtle variations in sympathovagal balance using ECG recordings. The method enhances HRV analysis by focusing the quantification of respiratory sinus arrhythmia (RSA) gain on the respiratory frequency. To this end, instantaneous respiratory frequency was obtained with ECG-derived respiration (EDR) and was used for variable frequency complex demodulation (VFCDM) of R-R intervals to extract RSA. The ability to detect cognitive stress in 27 subjects (athletes and nonathletes) was taken as a quality criterion to compare our method to other HRV analyses: Root mean square of successive differences, Fourier transform, wavelet transform, and scaling exponent. Three computer-based tasks from MATB-II were used to induce cognitive stress. Sympathovagal index (HF_nu) computed with our method better discriminates cognitive tasks from baseline, as indicated by P values and receiver operating characteristic curves. Here, transient decreases in respiratory frequency have shown to bias classical HRV indices, while only EDR-VFCDM consistently exhibits the expected decrease in the HF_nu index with cognitive stress in both groups and all cognitive tasks. We conclude that EDR-VFCDM is robust against atypical respiratory profiles, which seems relevant to assess variations in mental demand. Given the variety of individual respiratory profiles reported especially in highly trained athletes and patients with chronic respiratory conditions, EDR-VFCDM could better perform in a wide range of applications.

Time-varying assessment of heart rate variability parameters using respiratory information

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are estimated using smoothed extended Kalman filtering. Results for different synthetic data show that our proposed joint model outperforms the classical AR modeling in estimation of HRV parameters especially in the case of low respiration rate. In addition, the possibility of using pulse transit time (PTT) and the amplitude of photoplethysmogram (PPG_amp) as surrogates of the input respiratory signal has been investigated. To this end, electrocardiogram (ECG), PPG and respiration have been recorded from 21 healthy subjects (10 males and 11 females, mean age 27.5 ± 4.1) during normal and deep respiration. Results show that indeed PTT and PPG_amp offer good potential to be used as references for respiratory-related variations of HR, thus avoiding additional devices for recording respiration.

Heart rate variability and suicidal behavior

Identification of biological indicators of suicide risk is important given advantages of biomarker-based models. Decreased high frequency heart rate variability (HF HRV) may be a biomarker of suicide risk. The aim of this research was to determine whether HF HRV differs between suicide attempters and non-attempters. Using the Trier Social Stress Test (TSST), we compared HF HRV between females with and without a history of suicide attempt, all with a lifetime diagnosis of a mood disorder. To investigate a potential mechanism explaining association between HF HRV and suicide, we examined the association between self-reported anger and HF HRV. Results of an Area under the Curve (AUC) analysis showed attempters had a lower cumulative HF HRV during the TSST than non-attempters. In addition, while there was no difference in self-reported anger at baseline, the increase in anger was greater in attempters, and negatively associated with HF HRV. Results suggest that suicide attempters have a reduced capacity to regulate their response to stress, and that reduced capacity to regulate anger may be a mechanism through which decreased HF HRV can lead to an increase in suicide risk. Our results have implications for the prevention of suicidal behavior in at-risk populations.

High-Frequency Heart Rate Variability Linked to Affiliation with a New Group

This study tests the hypothesis that high levels of high-frequency heart rate variability (HF-HRV) predisposes individuals to affiliate with new groups. Resting cardiac physiological recordings were taken before and after experimental sessions to measure trait high-frequency heart rate variability as an index of dispositional autonomic influence on heart rate. Following an experimental manipulation of priming of caring-related words, participants engaged in a minimal group paradigm, in which they imagined being a member of one of two arbitrary groups, allocated money to members of the two groups, and rated their affiliation with the groups. High levels of HF-HRV were associated with ingroup favouritism while allocating money, an effect largely attributable to a positive relationship between HF-HRV and allocation of money to the ingroup, and less due to a negative relationship between HF-HRV and money allocation to the outgroup. HF-HRV was also associated with increased self-reported affiliation feelings for the ingroup but was unrelated to feelings towards the outgroup. These effects remained substantial even after controlling for age, gender, BMI, mood, caffeine consumption, time of day of data collection, smoking and alcohol behaviour, and respiration rate. Further, the effects were observed regardless of whether participants were primed with caring-related words or not. This study is the first to bridge a long history of research on ingroup favouritism to the relatively recent body of research on cardiac vagal tone by uncovering a positive association between HF-HRV and affiliation with a novel group.

Cardiorespiratory Information Dynamics during Mental Arithmetic and Sustained Attention

An analysis of cardiorespiratory dynamics during mental arithmetic, which induces stress, and sustained attention was conducted using information theory. The information storage and internal information of heart rate variability (HRV) were determined respectively as the self-entropy of the tachogram, and the self-entropy of the tachogram conditioned to the knowledge of respiration. The information transfer and cross information from respiration to HRV were assessed as the transfer and cross-entropy, both measures of cardiorespiratory coupling. These information-theoretic measures identified significant nonlinearities in the cardiorespiratory time series. Additionally, it was shown that, although mental stress is related to a reduction in vagal activity, no difference in cardiorespiratory coupling was found when several mental states (rest, mental stress, sustained attention) are compared. However, the self-entropy of HRV conditioned to respiration was very informative to study the predictability of RR interval series during mental tasks, and showed higher predictability during mental arithmetic compared to sustained attention or rest.