A study on the optimum order of autoregressive models for heart rate variability

Heart Rate Variability During REM Sleep is Associated with Reduced Negative Memory Bias

Emotional memories change over time, but the mechanisms supporting this change are not well understood. Memory consolidation during sleep has been shown to selectively prioritize negative experiences while forgetting neutral memories. Whereas studies examining the role of vagal heart rate variability (HRV) during waking in memory consolidation have shown that vagal HRV is associated with enhanced memory of positive experiences at the expense of negative ones. However, no studies have explored how HRV during sleep contributes to emotional memory processing. Accordingly, we aimed to investigate the neural and vagal contributions during sleep to the processing of neutral and negative memories. To do so, we examined the impact of pharmacological vagal suppression, using zolpidem, on overnight emotional memory consolidation in a double-blind, placebo-controlled, within-subject, cross-over design. Thirty-two participants encoded neutral and negative pictures in the morning, then were tested on picture recognition before and after a night of sleep. Zolpidem or a placebo drug were administered in the evening before overnight sleep, monitored with electroencephalography and electrocardiography. Results showed that higher vagal HRV in Non-Rapid Eye Movement Sleep slow wave sleep (NREM SWS) and Rapid Eye Movement Sleep (REM) was associated with greater overnight improvement for neutral pictures in the placebo condition. Additionally, higher vagal HRV during REM was associated with an emotional memory tradeoff (i.e., greater memory for neutral at the expense of negative images), indicating a potential role for REM vagal HRV in forming a positive memory bias overnight. As previously reported, zolpidem reduced vagal HRV during SWS and increased NREM sigma power, and this vagal suppression eliminated the positive memory bias. Lastly, we used a stepwise linear mixed effects regression framework to investigate how NREM sigma power and vagal HRV during REM independently explained the variance in the emotional memory tradeoff effect and found that including vagal HRV significantly improved the model's fit. Overall, these results suggest that neural and vagal signals synergistically interact in the processing of emotional memories, with REM vagal HRV playing a specific role in contributing to the positive memory bias.

Altered Heart Rate Variability During Mobile Game Playing and Watching Self-Mobile Gaming in Individuals with Problematic Mobile Game Use: Implications for Cardiac Health

Introduction

The surge in mobile gaming, fueled by smartphone and internet accessibility, lacks a comprehensive understanding of physiological changes during gameplay.

Methods

This study, involving 93 participants (average age 21.75 years), categorized them into Problematic Mobile Gaming (PMG) and non-problematic Mobile Gaming (nPMG) groups based on Problematic Mobile Gaming Questionnaire (PMGQ) scores. The PMGQ is a 12-item scale developed in Taiwan to assess symptoms of problematic mobile gaming. The research delved into heart rate variability (HRV) alterations during real-time mobile gaming and self-gaming video viewing.

Results

Results showed that the PMG group significantly presents a lower root mean square of successive differences (RMSSD), and High Frequency (lnHF) than does the nPMG group (F=4.73, p=0.03; F=10.65, p=0.002, respectively) at the baseline. In addition, the PMG group significantly displayed elevated HF and low-frequency to high-frequency (LF/HF) in the mobile-gaming (F=7.59, p=0.007; F=9.31, p=0.003) condition as well as in the watching self-gaming videos (F=9.75, p=0.002; F=9.02, p=0.003) than did the nPMG.

Conclusion

The study suggests targeted interventions to mitigate autonomic arousal, offering a potential avenue to address adverse effects associated with problematic mobile gaming behavior. The PMG group displayed increased craving scores after real-time mobile gaming and watching self-gaming video excerpts, unlike the nPMG group. Elevated LF/HF ratios in frequent gaming cases heightened autonomic arousal, presenting challenges in relaxation after mobile gaming. These findings contribute to a nuanced understanding of the complex interplay between mobile gaming activities, physiological responses, and potential intervention strategies.

Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population

Determination of prognosis in the triage process after traumatic brain injury (TBI) is difficult to achieve. Current severity measures like the Trauma and injury severity score (TRISS) and revised trauma score (RTS) rely on additional information from the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS) which may be inaccurate or delayed, limiting their usefulness in the rapid triage setting. We hypothesized that machine learning based estimations of GCS and ISS obtained through modeling of continuous vital sign features could be used to rapidly derive an automated RTS and TRISS. We derived variables from electrocardiograms (ECG), photoplethysmography (PPG), and blood pressure using continuous data obtained in the first 15 min of admission to build machine learning models of GCS and ISS (ML-GCS and ML-ISS). We compared the TRISS and RTS using ML-ISS and ML-GCS and its value using the actual ISS and GCS in predicting in-hospital mortality. Models were tested in TBI with systemic injury (head abbreviated injury scale (AIS) ≥ 1), and isolated TBI (head AIS ≥ 1 and other AIS ≤ 1). The area under the receiver operating characteristic curve (AUROC) was used to evaluate model performance. A total of 21,077 cases (2009-2015) were in the training set. 6057 cases from 2016 to 2017 were used for testing, with 472 (7.8%) severe TBI (GCS 3-8), 223 (3.7%) moderate TBI (GCS 9-12), and 5913 (88.5%) mild TBI (GCS 13-15). In the TBI with systemic injury group, ML-TRISS had similar AUROC (0.963) to TRISS (0.965) in predicting mortality. ML-RTS had AUROC (0.823) and RTS had AUROC 0.928. In the isolated TBI group, ML-TRISS had AUROC 0.977, and TRISS had AUROC 0.983. ML-RTS had AUROC 0.790 and RTS had AUROC 0.957. Estimation of ISS and GCS from machine learning based modeling of vital sign features can be utilized to provide accurate assessments of the RTS and TRISS in a population of TBI patients. Automation of these scores could be utilized to enhance triage and resource allocation during the ultra-early phase of resuscitation.

CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions

CardioRVAR is a new R package designed for the complete evaluation of closed-loop cardiovascular interactions and baroreflex sensitivity estimated from continuous non-invasive heart rate and blood pressure recordings. In this work, we highlight the importance of this software tool in the context of human cardiovascular and autonomic neurophysiology. A summary of the main algorithms that CardioRVAR uses are reviewed, and the workflow of this package is also discussed. We present the results obtained from this tool after its application in three clinical settings. These results support the potential clinical and scientific applications of this tool. The open-source tool can be downloaded from a public GitHub repository, as well as its specific Shiny application, CardioRVARapp. The open-source nature of the tool may benefit the future continuation of this work.

Neonatal Seizure Detection Using a Wearable Multi-Sensor System

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant's movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children's Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications

BACKGROUND

Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, existing algorithmic methods of HRV analysis do not check such eligibility and require manual identification of eligible window (portion of ECG record) to ensure reliability.

OBJECTIVE

We aimed to propose a robust algorithm with a sliding window feature to automate the identification of an eligible window, if available, which compute HRV parameters within that window obviating manual input.

METHODS

The proposed algorithm classifies each window as either eligible or ineligible. With a window classified eligible, we stop sliding through the record, otherwise we move to the next window and repeat the eligibility identification process, until either an eligible window is found, or all windows are exhausted.

RESULTS

When evaluated on random subset of 100 records from MIMIC-III waveform database, the proposed algorithm excluded every ineligible record, and missed only 1.25% of eligible ones. The HRV parameters computed using proposed method closely approximated the standard HRV analysis with Pearson correlation coefficients (ideally one) and fractions of variance unexplained (ideally zero) ranging from 96.3% to 99.8% and 0.34% to 7.43%, respectively.

CONCLUSIONS

When translated into practice, proposed algorithm will reduce clinicians'' burden without compromising the accuracy of HRV analysis, potentially leading to its wider adoption.

Interactive Cardio System for Healthcare Improvement

The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people's quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered.

Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach

Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824-0.877) and 0.84 (0.812-0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688-0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use.

How Reliable Are Ultra-Short-Term HRV Measurements during Cognitively Demanding Tasks?

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements' reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland-Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

Measuring Heart Rate Variability Using Facial Video

Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini–Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods.

Theil Entropy as a Non-Lineal Analysis for Spectral Inequality of Physiological Oscillations

Theil entropy is a statistical measure used in economics to quantify income inequalities. However, it can be applied to any data distribution including biological signals. In this work, we applied different spectral methods on heart rate variability signals and cellular calcium oscillations previously to Theil entropy analysis. The behavior of Theil entropy and its decomposable property was investigated using exponents in the range of [-1, 2], on the spectrum of synthetic and physiological signals. Our results suggest that the best spectral decomposition method to analyze the spectral inequality of physiological oscillations is the Lomb-Scargle method, followed by Theil entropy analysis. Moreover, our results showed that the exponents that provide more information to describe the spectral inequality in the tested signals were zero, one, and two. It was also observed that the intra-band component is the one that contributes the most to total inequality for the studied oscillations. More in detail, we found that in the state of mental stress, the inequality determined by the Theil entropy analysis of heart rate increases with respect to the resting state. Likewise, the same analytical approach shows that cellular calcium oscillations present on developing interneurons display greater inequality distribution when inhibition of a neurotransmitter system is in place. In conclusion, we propose that Theil entropy is useful for analyzing spectral inequality and to explore its origin in physiological signals.

Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.

Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.

Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.

Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p < 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p < 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p < 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p < 0.01).

Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

Diminished cognitive control in Internet gaming disorder: A multimodal approach with magnetic resonance imaging and real-time heart rate variability

OBJECTIVE

Recently, the addiction to online games, classified as Internet gaming disorder (IGD) on DSM-V, has emerged as an important mental health problem. The loss of control over gaming in IGD is associated with diminished cognitive control. This study aimed to link the neurobiological mechanism reflected by brain imaging and the diminished cognitive control reflected by heart rate variability (HRV) measurements during real-time gameplay.

METHODS

HRV was assessed in 33 young males with IGD and 29 controls while playing their favorite games. Seed-based functional connectivity (FC) was evaluated in the dorsolateral prefrontal cortex, anterior cingulate cortex, and dorsal striatum. Associations between HRV and alterations in FC were tested.

RESULTS

Individuals with IGD showed a reduction of high-frequency HRV during real-time gaming, which is correlated with self-reported severity of IGD. Subjects with IGD showed decreased FC between the right dorsolateral prefrontal cortex and the right inferior frontal gyrus, corresponding to the cognitive control network. They showed decreased FC between the right anterior cingulate cortex and the superior parietal lobule. They also showed increased FC between the left dorsal putamen and the postcentral gyrus, corresponding to the sensorimotor network. Game-related high-frequency HRV was correlated with dorsolateral prefrontal cortex-inferior frontal gyrus connectivity.

CONCLUSION

The diminished cognitive control reflected by HRV measurements during real-time gameplay was associated with FC alterations, involving a weak FC in the cognitive control network. Individuals with IGD may have less cognitive control, particularly when playing games, and consequently end up playing games in a habitual manner rather than in a goal-oriented manner.

Methodology for the prediction of paroxysmal atrial fibrillation based on heart rate variability feature analysis

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia, as its prevalence increases with age, and its initial stage is paroxysmal atrial fibrillation (PAF). This pathology usually triggers hemodynamic disorders that can generate cerebrovascular accidents (CVA), causing morbidity and even death. The aim of this study is to predict the occurrence of PAF episodes in order to take precautions to prevent PAF episodes. The PhysioNet AFPDB prediction database was used to extract 77 heart rate variability (HRV) features using time domain, geometrical analysis, Poincaré plot, nonlinear analysis, detrended fluctuation analysis, autoregressive modeling, fast Fourier transform (FFT), Lomb-Scargle periodogram, wavelet packet transform (WPT) and bispectrum measurements. The number of features was reduced using the near-zero value, correlation, and recursive feature elimination (RFE) methods for time windows of 1, 2, 5, 10, and 30 min. Feature selection was performed using backwards selection, genetic algorithm, analysis of variance (ANOVA), and non-dominated sorting genetic algorithm (NSGA-III) methods, and then random forest, conditional random forest, k-nearest neighbor (KNN), and support vector machine (SVM) classification algorithms were applied and evaluated using 10-fold cross-validation. The proposed method achieved a precision of 93.24% with a 5-minute window and 89.21% with a 2-minute window, improving performance in predicting PAF when compared with similar studies in the literature.

Alternations in the Cardiovascular Autonomic Regulation and Growth Factors in Autism

Autism spectrum disorder (ASD) represents a serious neurodevelopmental disorder associated with autonomic nervous system dysregulation. The aim was to study complex cardiovascular autonomic regulation using heart rate variability (HRV) and systolic blood pressure variability (SBPV) linear/non-linear analysis at rest and during orthostasis, and to assess plasma levels of epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) in autistic children. Twenty-five ASD boys and 25 age and gender-matched children at the age 7-15 years were examined. After venous blood taking, continuous ECG and blood pressure biosignals were recorded at rest and during orthostasis. Evaluated parameters: RR intervals, high- and low-frequency band of HRV spectral analysis (HF-HRV, LF-HRV), symbolic dynamics parameters 0V%, 1V%, 2LV%, 2UV%, low- and high-frequency band of SBPV (LF-SBPV, HF-SBPV), systolic, diastolic, mean blood pressure, EGF, VEGF plasma levels. RR intervals were significantly shortened and the HF-HRV, LF-SBPV, HF-SBPV parameters were significantly lower at rest, the HF-HRV and LF-SBPV remained lower during orthostasis in autistic children compared to controls (p<0.05). EGF plasma levels were significantly lower in ASD compared to controls (p=0.046). No significant differences were found in remaining parameters. Our study revealed tachycardia, cardiovagal underactivity, and blunted sympathetic vasomotor regulation at rest and during orthostasis in autistic children. Additionally, complex heart rate dynamics are similar in autistic children than controls. Furthermore, EGF was reduced in autistic children without significant correlations with any autonomic parameters. We suggest that the abnormal complex cardiovascular reflex control could contribute to understanding the pathway linking autonomic features and autism.

Age-related losses in cardiac autonomic activity during a daytime nap

In healthy, young individuals, a reduction in cardiovascular output and a shift from sympathetic to parasympathetic (vagal) dominance is observed from wake into stages of nocturnal and daytime sleep. This cardiac autonomic profile, measured by heart rate variability (HRV), has been associated with significant benefits for cardiovascular health. Aging is associated with decreased nighttime sleep quality and lower parasympathetic activity during both sleep and resting. However, it is not known whether age-related dampening of HRV extends to daytime sleep, diminishing the cardiovascular benefits of naps in the elderly. Here, we investigated this question by comparing the autonomic activity profile between young and older healthy adults during a daytime nap and a similar period of wakefulness (quiet wake; QW). For each condition, from the electrocardiogram (ECG), we obtained beat-to-beat HRV intervals (RR), root mean square of successive differences between adjacent heart-beat-intervals (RMSSD), high-frequency (HF), low-frequency (LF) power, and total power (TP), HF normalized units (HFnu ), and the LF/HF ratio. As previously reported, young subjects showed a parasympathetic dominance during NREM, compared with REM, prenap rest, and WASO. Moreover, older, compared to younger, adults showed significantly lower vagally mediated HRV (measured by RMSSD, HF, HFnu ) during NREM. Interestingly, however, no age-related differences were detected during prenap rest or QW. Altogether, our findings suggest a sleep-specific reduction in parasympathetic modulation that is unique to NREM sleep in older adults.

The effect of ambient sounds on decision-making and heart rate variability in autism

LAY ABSTRACT

Many autistic people report difficulties making decisions during everyday tasks, such as shopping. To examine the effect of sounds on decision-making, we developed a supermarket task where people watched a film shown from the shopper's perspective and were asked to make decisions between different products. The task was divided into three sections and participants completed each section in a different auditory environment: (1) no sounds, (2) non-social sounds (e.g. fridges humming) and (3) social sounds (e.g. people talking). Thirty-eight autistic and 37 neurotypical adults took part. We measured decision-making by examining how long it took to make a decision and how consistent people were with their decisions. We also measured heart rate variability because this biological response provides a measure of anxiety. After the supermarket shopping task, participants told us in their own words about their experiences. Autistic participants said that they found the non-social and social sound conditions more difficult than the no sound condition, and autistic participants found the social sound condition more negative than neurotypical participants. However, decision-making and heart rate variability were similar for autistic and neurotypical participants across the sound conditions, suggesting that these measures may not have been sensitive enough to reflect the experiences the autistic participants reported. Further research should consider alternative measures to explore the experiences reported by autistic people to help us understand which specific aspects of the environment autistic people are sensitive to. This, in turn, may enable more specific and evidence-based autism-friendly changes to be made.

Shift Work Adaptation Among Police Officers: The BCOPS Study

Few studies have examined shiftwork adaptation among police officers or potential differences in disease biomarkers among adapted and maladapted shiftworkers. This study characterized shiftwork adaptation among 430 police officers from the Buffalo Cardio-Metabolic Occupational Police Stress (BCOPS) study. Police officers working fixed night shifts with symptoms characteristic of adaptation and maladaptation were identified using latent class analysis (n = 242). Two approaches were applied, one with police-specific symptoms and another using more general symptoms as shiftwork adaptation indicators. Biomarkers of inflammation, heart rate variability, and cardiometabolic risk were then compared between shiftwork adaptation groups, and with officers working day shifts, after adjusting for confounding. When analyses included police-specific symptoms, maladapted shiftworkers (n = 73) had more self-reported stress, sleep disturbances, fatigue, and less social support than adapted shiftworkers (n = 169). Using more general symptoms, maladapted officers (n = 56) reported more stress and depression, and less social support than adapted officers (n = 186). In police-specific models, adjusted (least-squares) means (± standard error) of circulating interleukin-6 (IL-6) concentrations in maladapted officers (0.8 ± 0.1 ln[pg/ml]) were modestly elevated relative to adapted shiftworkers (0.7 ± 0.1 ln[pg/ml], p = .09) and relative to permanent day workers (0.5 ± 0.1 ln[pg/ml], p ≤ 0.01), and leptin levels in maladapted officers (9.6 ± 0.1 ln[pg/ml]) exceeded those in the adapted (9.4 ± 0.1 ln[pg/ml], p ≤ 0.01) and day shift groups (9.4 ± 0.1 ln[pg/ml], p = .03). In the general model, adjusted mean tumor necrosis factor-alpha (TNF-α) concentrations among maladapted officers (5.6 ± 0.23 pg/ml) exceeded the adapted (4.8 ± 0.2 pg/ml, p ≤ 0.01) and day workers (5.0 ± 0.2 pg/ml, p = .04), and insulin among maladapted officers was higher (2.4 ± 0.1 ln[uu/ml]) than the adapted group (1.8 ± 0.1 ln[uu/ml], p = .03). No differences were observed for the other biomarkers. The results suggest that maladaptation among police officers working fixed night shifts may lead to increases in leptin, insulin, IL-6, and TNF-α; however, the cross-sectional design and possible residual confounding preclude interpretation of cause and effect. Prospective studies are planned to further characterize the relationship between shiftwork maladaptation and biomarkers of chronic disease risk in this police officer cohort.

Cardiorespiratory synchronisation and systolic blood pressure correlation of peripheral arterial stiffness during endoscopic thoracic sympathectomy

Muscle sympathetic nerve activity (MSNA) is known as an effective measure to evaluate peripheral sympathetic activity; however, it requires invasive measurement with the microneurography method. In contrast, peripheral arterial stiffness affected by MSNA is a measure that allows non-invasive evaluation of mechanical changes of arterial elasticity. This paper aims to clarify the features of peripheral arterial stiffness to determine whether it inherits MSNA features towards non-invasive evaluation of its activity. To this end, we propose a method to estimate peripheral arterial stiffness [Formula: see text] at a high sampling rate. Power spectral analysis of the estimated [Formula: see text] was then performed on data acquired from 15 patients ([Formula: see text] years) who underwent endoscopic thoracic sympathectomy. We examined whether [Formula: see text] exhibited the features of MSNA where its frequency components synchronise with heart and respiration rates and correlates with the low-frequency component of systolic blood pressure. Regression analysis revealed that the local peak frequency in the range of heartbeat frequency highly correlate with the heart rate ([Formula: see text], [Formula: see text]) where the regression slope was approximately 1 and intercept was approximately 0. Frequency analysis then found spectral peaks of [Formula: see text] approximately 0.2 Hz that correspond to the respiratory cycle. Finally, cross power spectral analysis showed a significant magnitude squared coherence between [Formula: see text] and systolic blood pressure in the frequency band from 0.04 to 0.2 Hz. These results indicate that [Formula: see text] inherits the features observed in MSNA that require invasive measurements, and thus [Formula: see text] can be an effective non-invasive substitution for MSNA measure.

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations.

Inter- and intra-researcher reproducibility of heart rate variability parameters in three human cohorts

Heart rate variability (HRV) is a valid and non-invasive indicator of cardiac autonomic nervous system functioning. Short-term HRV recordings (e.g., 10 min long) produce data that usually is manually processed. Researcher subjective decision-making on data processing could produce inter- or intra-researcher differences whose magnitude has not been previously quantified in three independent human cohorts. This study examines the inter- and intra-researcher reproducibility of HRV parameters (i.e., the influence of R–R interval selection by different researchers and by the same researcher in different moments on the quantification of HRV parameters, respectively) derived from short-term recordings in a cohort of children with overweight/obesity, young adults and middle-age adults. Participants were recruited from 3 different studies: 107 children (10.03 ± 1.13 years, 58% male), 132 young adults (22.22 ± 2.20 years, 33% males) and 73 middle-aged adults (53.62 ± 5.18 years, 48% males). HRV was measured using a Polar RS800CX heart rate monitor. The intraclass correlation coefficient (ICC) ranged from 0.703 to 0.989 and from 0.950 to 0.998 for inter-and intra-researcher reproducibility, respectively. Limits of agreement for HRV parameters were higher for the inter-researcher processing compared with the intra-researcher processing. On average, the intra-researcher differences were 31%, 62%, and 80% smaller than the inter-researchers differences based on Coefficient of Variation in children, young and middle-aged adults, respectively. Our study provides the quantification of the inter-researcher and intra-researcher differences in three independent human cohorts, which could elicit some clinical relevant differences for HRV parameters. Based on our findings, we recommend the HRV data signal processing to be performed always by the same trained researcher and we postulate a development of algorithms for an automatic ECG selection.

The Source of Heart Rhythm Changes Caused by Swallowing

Swallowing can lead to the development of syncope in people who have difficulty swallowing during food intake. It has shown that even spontaneous saliva swallowing can change heart rate variability (HRV). Recently, it has been suggested that changes in heart rate during swallowing may be caused by respiratory activities. In this study, the hypothesis that swallowing induced HRV are caused from breathing changes during swallowing has been tested. For this purpose, electrocardiogram (ECG), chest circumference (respiration) signals and swallowing sounds were recorded simultaneously from 20 subjects. Subjects were asked not to swallow their saliva in the first 4 min of the experiment and to swallow them several times in the next 4 min. To observe respiratory effects on HRV during swallowing, a detailed cardio-respiratory system mathematical model was used. By applying recorded chest circumference signal to the mathematical model, respiration induced HRV changes were obtained. The HRV parameters of with and without swallowing regions of the real (obtained from ECG) and model-HRV (obtained from mathematical model) were compared by paired Student t test. Statistical differences seen in the real-HRV between the swallowing and non-swallowing regions (SDNN, LF power, approximate entropy) were not observed in the model-HRV. Considering that the only factor constituting HRV in the mathematical model is respiration, it was concluded that swallowing changes HRV with a mechanism other than breathing changes.

Autonomic Activity during a Daytime Nap Facilitates Working Memory Improvement

Recent investigations have implicated the parasympathetic branch of the autonomic nervous system in higher-order executive functions. These actions are purported to occur through autonomic nervous system's modulation of the pFC, with parasympathetic activity during wake associated with working memory (WM) ability. Compared with wake, sleep is a period with substantially greater parasympathetic tone. Recent work has reported that sleep may also contribute to improvement in WM. Here, we examined the role of cardiac parasympathetic activity during sleep on WM improvement in healthy young adults. Participants were tested in an operation span task in the morning and evening, and during the intertest period, participants experienced either a nap or wake. We measured high-frequency heart rate variability as an index of cardiac, parasympathetic activity during both wake and sleep. Participants showed the expected boost in parasympathetic activity during nap, compared with wake. Furthermore, parasympathetic activity during sleep, but not wake, was significantly correlated with WM improvement. Together, these results indicate that the natural boost in parasympathetic activity during sleep may benefit gains in prefrontal executive function in young adults. We present a conceptual model illustrating the interaction between sleep, autonomic activity, and prefrontal brain function and highlight open research questions that will facilitate understanding of the factors that contribute to executive abilities in young adults as well as in cognitive aging.

A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning

Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.

Respiratory Sinus Arrhythmia as an Index of Cardiac Vagal Control in Mitral Valve Prolapse

Respiratory sinus arrhythmia (RSA), i.e. heart rate (HR) variations during inspiration and expiration, is considered as a noninvasive index of cardiac vagal control. Mitral valve prolapse (MVP) could be associated with increased cardiovascular risk; however, the studies are rare particularly at adolescent age. Therefore, we aimed to study cardiac vagal control indexed by RSA in adolescent patients suffering from MVP using short-term heart rate variability (HRV) analysis. We examined 12 adolescents (girls) with MVP (age 15.9±0.5 years) and 12 age and gender matched controls. Resting ECG was continuously recorded during 5 minutes. Evaluated HRV indices were RR interval (ms), rMSSD (ms), pNN50 (%), log HF (ms(2)), peak HF (Hz) and respiratory rate (breaths/min). RR interval was significantly shortened in MVP group compared to controls (p=0.004). HRV parameters-rMSSD, pNN50 and log HF were significantly lower in MVP compared to controls (p=0.017, p=0.014, p= 0.015 respectively). Our study revealed reduced RSA magnitude indicating impaired cardiac vagal control in MVP already at adolescent age that could be crucial for early diagnosis of cardiovascular risk in MVP.

Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on physiological signals. It has been reported that OSA is, along with autonomic nervous system (ANS) dysfunction and heart rate variability (HRV), a useful tool for ANS assessment. Therefore, in this paper, eight novel indices of short-time HRV are extracted for OSA detection, which are based on the proposed multi-bands time-frequency spectrum entropy (MTFSE) method. In the MTFSE, firstly, the power spectrum of HRV is estimated by the Burg-AR model, and the time-frequency spectrum image (TFSI) is obtained. Secondly, according to the physiological significance of HRV, the TFSI is divided into multiple sub-bands according to frequency. Last but not least, by studying the Shannon entropy of different sub-bands and the relationships among them, the eight indices are obtained. In order to validate the performance of MTFSE-based indices, the Physionet Apnea-ECG database and K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) classification methods are used. The SVM classification method gets the highest classification accuracy, its average accuracy is 91.89%, the average sensitivity is 88.01%, and the average specificity is 93.98%. Undeniably, the MTFSE-based indices provide a novel idea for the screening of OSA disease.

Heart Rate Variability reveals the fight between racially biased and politically correct behaviour

In this study, we explored vagally-mediated heart rate variability (vmHRV) responses, a psychophysiological index of cognitive self-regulatory control, to map the dynamics associated with empathic responses for pain towards an out-group member. Accordingly, Caucasian participants were asked to judge the experience of African and Caucasian actors touched with either a neutral or a harmful stimulus. Results showed that (1) explicit judgment of pain intensity in African actors yielded higher rating score and (2) took longer time compared to Caucasian actors, (3) these behavioural outcomes were associated with a significant increment of RMSSD, Log-HF-HRV and HF-HRV n.u., (4) resting HF-HRV n.u. predicted the participants’ lag-time to judge painful stimulations delivered to African actors. Interestingly, these dynamics were associated with a measure of implicit racial attitudes and were, in part, abolished when participants performed a concurrent task during videos presentation. Taken together our results support the idea that a cognitive effort is needed to self-regulate our implicit attitude as predicted by the ‘Contrasting Forces Model’.

The influence of power posing on cardiac vagal activity

The effects of power posing on hormonal reactions such as testosterone and cortisol have been widely investigated, however, its effects on the autonomic nervous system are rather unknown. Consequently, the aim of this study was to investigate the influence of power posing on cardiac vagal activity (CVA), as indexed by heart rate variability. It was hypothesized that high power poses (HPP) would increase CVA, whereas low power poses (LPP) would decrease CVA, given power posing is expected to decrease stress. Participants (N = 56) performed a total of four power poses, a combination of two power conditions (high vs. low) and two body positions (sitting vs. standing) for 1 min each, in a randomized order. In addition, for each power pose participants were given a role description. Contrary to our hypothesis, CVA decreased significantly during HPP in comparison to the resting measures before and after HPP, and CVA did not change during LPP. Moreover, while holding the power pose, CVA was higher in the LPP than in the HPP condition. Regarding subjective measures our hypotheses were confirmed, felt power was significantly higher after HPP than after LPP. Additionally, perceived stress was higher after LPP than after HPP. Taken together, these results suggest that the immediate impact of PP on the autonomic nervous system is more likely to influence a higher state of activation within the body instead of increasing resources to cope with stress as indexed by CVA, which may be seen only on a more long-term basis.

Downregulation of Orexin Receptor in Hypothalamic Paraventricular Nucleus Decreases Blood Pressure in Obese Zucker Rats

Background

Orexin and its receptors are critical regulating sympathetic vasomotor tone under physiological and pathophysiological conditions. Orexin receptor 1 (OXR1) is upregulated in the paraventricular nucleus (PVN) in the hypothalamus and contributes to increased sympathetic outflow in obese Zucker rats (OZRs). We hypothesized that silencing OXR1 expression in the PVN decreases heightened blood pressure and elevated sympathetic outflow in OZRs.

Methods and Results

An adeno‐associated virus (AAV) vector containing a short hairpin RNA (shRNA) targeting rat OXR1 was designed to silence OXR1 expression in the PVN. The AAV‐OXR1‐shRNA or scrambled shRNA was injected into the PVN in OZRs. The arterial blood pressure in free‐moving OZRs was continuously monitored by using a telemetry approach. The firing activity of spinally projecting PVN neurons in rat brain slices was recorded 3 to 4 weeks after injection of viral vectors. The free‐moving OZRs treated with AAV‐OXR1‐shRNA had markedly lower OXR1 expression and lower mean arterial blood pressure, heart rate, and ratio of low‐ to high‐frequency components of heart rate variability compared with OZRs treated with scrambled shRNA. Furthermore, AAV‐OXR1‐shRNA treatment markedly reduced renal sympathetic nerve activity and attenuated sympathoexcitatory response induced by microinjection of orexin A into the PVN. In addition, treatment with AAV‐OXR1‐shRNA substantially decreased the basal firing activity of spinally projecting PVN neurons in OZRs and attenuated the excitatory effect of orexin A on the firing activity of these neurons.

Conclusions

These data suggest that chronic downregulation of OXR1 expression in the PVN reduces sympathetic vasomotor tone in obesity‐related hypertension.

Normative Values for Heart Rate Variability Parameters in School-Aged Children: Simple Approach Considering Differences in Average Heart Rate

Background: Heart rate variability (HRV) analysis is a clinical tool frequently used to characterize cardiac autonomic status. The aim of this study was to establish normative values for short-term HRV parameters by considering their main determinants in school-aged children.

Methods: Five-minute electrocardiograms were taken from 312 non-athlete children (153 boys) at age of 6 to 13 years for computation of conventional time- and frequency-domain HRV parameters. Heart rate (HR), respiratory rate, age, body mass index, and sex were considered as their potential determinants. Multiple regression analysis revealed that HR was the principal predictor of all standard HRV indices. To develop their universal normative limits, standard HRV parameters were corrected for prevailing HR.

Results: The HRV correction for HR yielded the parameters which became independent on both sex and HR, and only poorly dependent on age (with small effect size). Normal ranges were calculated for both time- and frequency-domain indices (the latter computed with either fast Fourier transform and autoregressive method). To facilitate recalculation of standard HRV parameters into corrected ones, a calculator was created and attached as a Supplementary Material that can be downloaded and used for both research and clinical purposes.

Conclusion: This study provides HRV normative values for school-aged children which have been developed independently of their major determinants. The calculator accessible in the Supplementary Material can considerably simplify determination if HRV parameters accommodate within normal limits.

PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data

Background: The time variation between consecutive heartbeats is commonly referred to as heart rate variability (HRV). Loss of complexity in HRV has been documented in several cardiovascular diseases and has been associated with an increase in morbidity and mortality. However, the mechanisms that control HRV are not well understood. Animal experiments are the key to investigating this question. However, to date, there are no standard open source tools for HRV analysis of mammalian electrocardiogram (ECG) data and no centralized public databases for researchers to access. Methods: We created an open source software solution specifically designed for HRV analysis from ECG data of multiple mammals, including humans. We also created a set of public databases of mammalian ECG signals (dog, rabbit and mouse) with manually corrected R-peaks (>170,000 annotations) and signal quality annotations. The platform (software and databases) is called PhysioZoo. Results: PhysioZoo makes it possible to load ECG data and perform very accurate R-peak detection (F 1 > 98%). It also allows the user to manually correct the R-peak locations and annotate low signal quality of the underlying ECG. PhysioZoo implements state of the art HRV measures adapted for different mammals (dogs, rabbits, and mice) and allows easy export of all computed measures together with standard data representation figures. PhysioZoo provides databases and standard ranges for all HRV measures computed on healthy, conscious humans, dogs, rabbits, and mice at rest. Study of these measures across different mammals can provide new insights into the complexity of heart rate dynamics across species. Conclusion: PhysioZoo enables the standardization and reproducibility of HRV analysis in mammalian models through its open source code, freely available software, and open access databases. PhysioZoo will support and enable new investigations in mammalian HRV research. The source code and software are available on www.physiozoo.com.

Analysis of ischaemic crisis using the informational causal entropy-complexity plane

In the present work, an ischaemic process, mainly focused on the reperfusion stage, is studied using the informational causal entropy-complexity plane. Ischaemic wall behavior under this condition was analyzed through wall thickness and ventricular pressure variations, acquired during an obstructive flow maneuver performed on left coronary arteries of surgically instrumented animals. Basically, the induction of ischaemia depends on the temporary occlusion of left circumflex coronary artery (which supplies blood to the posterior left ventricular wall) that lasts for a few seconds. Normal perfusion of the wall was then reestablished while the anterior ventricular wall remained adequately perfused during the entire maneuver. The obtained results showed that system dynamics could be effectively described by entropy-complexity loops, in both abnormally and well perfused walls. These results could contribute to making an objective indicator of the recovery heart tissues after an ischaemic process, in a way to quantify the restoration of myocardial behavior after the supply of oxygen to the ventricular wall was suppressed for a brief period.

Comparing the cardiac autonomic activity profile of daytime naps and nighttime sleep

Heart rate variability (HRV) is a reliable technique to evaluate autonomic activity and shows marked changes across a night of sleep. Previous nighttime sleep findings report changes in HRV during non-rapid eye movement sleep (NREM), which have been associated with cardiovascular health benefits. Daytime sleep, however, has been linked with both positive and negative cardiovascular outcomes. Yet, no studies have directly compared HRV profiles during an ecologically-valid daytime nap in healthy, well-rested adults to that of nighttime sleep. Using a within-subjects design, 32 people took a daytime nap and slept overnight in the lab at least one week apart; both sleep sessions had polysomnography, including electrocardiography (ECG), recorded. We measured inter-beat intervals (RR), total power (TP), low frequency power (LF; .04-.15 Hz), and high frequency power (HF; .15-.40 Hz) components of HRV during NREM and rapid eye movement (REM) sleep. Compared to the nap, we found longer RR intervals and decreased heart rate during the night for both Stage 2 and SWS and increased TP, LF and HF power during nighttime Stage 2 sleep only; however, no differences in the LFHF ratio or normalized HF power were found between the nap and the night. Also, no differences in REM sleep between the nap and night were detected. Similar relationships emerged when comparing the nap to one cycle of nighttime sleep. These findings suggest that longer daytime naps, with both SWS and REM, may provide similar cardiovascular benefits as nocturnal sleep. In light of the on-going debate surrounding the health benefits and/or risks associated with napping, these results suggest that longer daytime naps in young, healthy adults may support cardiac down-regulation similar to nighttime sleep. In addition, napping paradigms may serve as tools to explore sleep-related changes in autonomic activity in both healthy and at-risk populations.

Assessment of tobacco smoke effects on neonatal cardiorespiratory control using a semi-automated processing approach

A semi-automated processing approach was developed to assess the effects of early postnatal environmental tobacco smoke (ETS) on the cardiorespiratory control of newborn lambs. The system consists of several steps beginning with artifact rejection, followed by the selection of stationary segments, and ending with feature extraction. This approach was used in six lambs exposed to 20 cigarettes/day for the first 15 days of life, while another six control lambs were exposed to room air. On postnatal day 16, electrocardiograph and respiratory signals were obtained from a 6-h polysomnographic recording. The effects of postnatal ETS exposure on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were explored. The unique results suggest that early postnatal ETS exposure increases respiratory rate variability and decreases the coupling between cardiac and respiratory systems. Potentially harmful consequences in early life include unstable breathing and decreased adaptability of cardiorespiratory function, particularly during early life challenges, such as prematurity or viral infection. Graphical abstract ᅟ.

Altered Heart Rate Variability During Gaming in Internet Gaming Disorder

Internet gaming disorder (IGD) is characterized by addiction to online gaming and reduced executive control, particularly when individuals are exposed to gaming-related cues. Executive control can be measured as vagally mediated heart rate variability (HRV), which corresponds to variability in the time interval between heart beats. In this study, we investigated whether individuals with IGD have altered HRV while playing online games. We hypothesized that while gaming, individuals with IGD would exhibit phasic suppression of vagally mediated HRV, which would reflect executive control dysfunction during game play. To test this, we measured the changes of HRV when young males with IGD were engaged in real-time online gaming. The changes of HRV were associated with the severity of IGD assessed by self-reports and prefrontal gray matter volume (GMV) calculated by voxel-based morphometry. We included 23 IGD subjects and 18 controls in our analyses. Changes in HRV were not statistically different between IGD subjects and controls. Within the IGD group, however, subjects showed significant decreases in high-frequency (HF) HRV during gaming. Furthermore, the degree of decrease correlated with IGD severity and prefrontal GMV. Importantly, this phasic suppression of HF-HRV in response to gaming did not occur in control subjects. In conclusion, young males with IGD showed an altered HRV response while playing an online game, reflecting their difficulties in executive control over gaming. The dynamics between executive control and reward seeking may be out of balance during game play in IGD.

Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness

OBJECTIVE

Appropriate preprocessing for detecting and removing outlier heartbeats and spectral transformation is essential for deriving heart rate variability (HRV) indices from cardiac monitoring data with high accuracy. The objective of this study is to evaluate agreement between standard preprocessing methods for cardiac monitoring data used to detect outlier heartbeats and perform spectral transformation, in relation to estimating HRV indices for drivers at different stages of sleepiness.

METHODS

The study analyzed more than 3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. Electrocardiography (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The Pan-Tompkins algorithm was used for peak detection of heartbeats from ECG data. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat interval deviating >30% from the mean of the 4 previous intervals, and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain; these methods were (1) the Fourier transform; (2) an autoregressive model; and (3) the Lomb-Scargle periodogram. The preprocessing methods were compared quantitatively and by assessing agreement between estimations of 13 common HRV indices using Bland-Altman plots and paired Student's t-tests.

RESULTS

The PC method detected more than 4 times as many outliers (0.28%) than SD (0.065%). Most HRV indices derived using different preprocessing methods exhibited significant systematic (P <.05) and substantial random variations.

CONCLUSIONS

The standard preprocessing methods for HRV data for outlier heartbeat detection and spectral transformation show low levels of agreement. This finding implies that, prior to designing algorithms for detection of sleepy drivers based on HRV analysis, the impact of different preprocessing methods and combinations thereof on driver sleepiness assessment needs to be studied.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity.

Altered Heart Rate Variability During Gameplay in Internet Gaming Disorder: The Impact of Situations During the Game

Internet gaming disorder (IGD) is characterized by a loss of control over gaming and a decline in psychosocial functioning derived from excessive gameplay. We hypothesized that individuals with IGD would show different autonomic nervous system (ANS) responses to the games than those without IGD. In this study, heart rate variability (HRV) was assessed in 21 young males with IGD and 27 healthy controls while playing their favorite Internet game. The subjects could examine the game logs to identify the most and least concentrated periods of the game. The changes in HRV during specific 5-min periods of the game (first, last, and high- and low-attention) were compared between groups via a repeated measures analysis of variance. Significant predictors of HRV patterns during gameplay were determined from stepwise multiple linear regression analyses. Subjects with IGD showed a significant difference from controls in the patterns of vagally mediated HRV, such that they showed significant reductions in high-frequency HRV, particularly during the periods of high attention and the last 5 min, compared with baseline values. A regression analysis showed that the IGD symptom scale score was a significant predictor of this reduction. These results suggest that an altered HRV response to specific gaming situations is related to addictive patterns of gaming and may reflect the diminished executive control of individuals with IGD while playing Internet games.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training).

Serial correlations in single-subject fMRI with sub-second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.

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.

Effect of spontaneous saliva swallowing on short-term heart rate variability (HRV) and reliability of HRV analysis

The effects of effortful swallowing and solid meal ingestions on heart rate variability (HRV) have been examined previously. The effects of spontaneous saliva swallowing on short-term HRV and reliability of HRV analysis have not been studied before. The effect of saliva swallowing on HRV analyses parameters [meanRRI, SDNN (standard deviation of normal-to-normal), LF (low frequency), HF (high frequency) powers, LH/HF] and the reliability of LF and HF powers were investigated by frequency, time-frequency and intraclass correlation coefficient (ICC) analyses. Electrocardiogram and swallowing signal that obtained from an electronic stethoscope placed on the necks of subjects were recorded simultaneously from 30 healthy and young volunteers in sitting position during 15 min. Spontaneous swallowing has been shown to significantly alter some HRV parameters (SDNN, LF power and LF/HF ratio). Time-frequency analysis results showed that the contribution of saliva swallowing to LF (1-58%) and HF (2-42%) powers could change significantly depending on the number of swallowing. The ICC of the LF and HF powers for the successive 5-min signal segments were found 0·89, 0·92, respectively. These values decreased to 0·73 and 0·90 in the subjects with more swallowing rate. When the analyses were made for 2-min signal periods, these values decreased to 0·63 and 0·67. We concluded that spontaneous saliva swallowing can change HRV parameters. We have also seen that changes in swallowing rate and use of short signal segments may reduce the reliability of HRV analyses.