Heart rate variability

Cardiac autonomic responses to transrectal examination and parlor milking of lame and nonlame dairy cows

We studied the changes in the heart rate variability of lame and nonlame dairy cows in response to transrectal examination (TRE) and parlor milking. We hypothesized that lame cows experience greater stress, manifested in heart rate variability parameters, that serves as an argument to promote more caution in the everyday handling of lame animals. In the study, we selected 55 lame (with lesions on at least one hoof, otherwise clinically healthy, with locomotion scores 4 and 5 of 5-point scores) and 55 nonlame (clinically healthy, with locomotion scores 1 and 2 of 5-point scores) cows. The heart rate, root mean square of successive beat-to-beat intervals (RMSSD), the normalized unit of the high-frequency (HF) component of the spectral analysis and Poincaré measures (SD2/SD1) were compared between lame and nonlame cows during 5 distinct stages of TRE and 7 stages of parlor milking. Heart rate, RMSSD, and SD2/SD1 showed similar patterns during TRE and milking, while HF remained fairly constant during the studied phases. No distinct RMSSD, HF, or SD2/SD1 changes were observed during the phases expected to elicit a stress response. Between-group differences were only observed in terms of HF. Baseline HF was higher in lame cows than in nonlame ones, and such a difference in direction was observed throughout the whole procedure. During milking and TRE, the heart rate, RMSSD, and SD2/SD1 values showed temporal changes in times of higher physical activity: moving to and waiting in the holding pen and moving into the milking stalls in the parlor for preparation in both lame and nonlame cows. The differences in baseline HF (normalized units) between lame and nonlame cows cannot be fully explained based on available information. The lack of a distinct, stress-related change in heart rate variability parameters in both groups can originate in methodological challenges inherent in animal heart rate variability measurements in field conditions.

IoT-Based Assessment of a Driver's Stress Level

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

Association of CETP Gene Polymorphisms and Haplotypes with Acute Heart Rate Response to Exercise

Polymorphisms in the cholesteryl ester transfer protein (CETP) gene are known to be strongly associated with increased cardiovascular risk, primarily through their effects on the lipid profile and consequently on atherosclerotic risk. The acute heart rate response (AHRR) to physical activity is closely related to individual cardiovascular health. This study aimed to investigate the effect of CETP gene polymorphisms on AHRR. Our analysis examines the association of five single nucleotide polymorphisms (SNPs; rs1532624, rs5882, rs708272, rs7499892, and rs9989419) and their haplotypes (H) in the CETP gene with AHRR in 607 people from the Hungarian population. Individual AHRR in the present study was assessed using the YMCA 3-min step test and was estimated as the difference between resting and post-exercise heart rate, i.e., delta heart rate (ΔHR). To exclude the direct confounding effect of the CETP gene on the lipid profile, adjustments for TG and HDL-C levels, next to conventional risk factors, were applied in the statistical analyses. Among the examined five SNPs, two showed a significant association with lower ΔHR (rs1532624-Cdominant: B = -8.41, p < 0.001; rs708272-Gdominant: B = -8.33, p < 0.001) and reduced the risk of adverse AHRR (rs1532624-Cdominant: OR = 0.44, p = 0.004; rs708272-Gdominant: OR = 0.43, p = 0.003). Among the ten haplotypes, two showed significant association with lower ΔHR (H3-CAGCA: B = -6.81, p = 0.003; H9-CGGCG: B = -14.64, p = 0.015) and lower risk of adverse AHRR (H3-CAGCA: OR = 0.58, p = 0.040; H9-CGGCG: OR = 0.05, p = 0.009) compared to the reference haplotype (H1-AGACG). Our study is the first to report a significant association between CETP gene polymorphisms and AHRR. It also confirms that the association of the CETP gene with cardiovascular risk is mediated by changes in heart rate in response to physical activity, in addition to its effect on lipid profile.

Deconstructing depression by machine learning: the POKAL-PSY study

Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic, social, and interpersonal impact of depression and its increasing prevalence, there is a need to improve its diagnosis and treatment in outpatient care. Various efforts have been made to isolate individual biological markers for depression to streamline diagnostic and therapeutic approaches. However, the intricate and dynamic interplay between neuroinflammation, metabolic abnormalities, and relevant neurobiological correlates of depression is not yet fully understood. To address this issue, we propose a naturalistic prospective study involving outpatients with unipolar depression, individuals without depression or comorbidities, and healthy controls. In addition to clinical assessments, cardiovascular parameters, metabolic factors, and inflammatory parameters are collected. For analysis we will use conventional statistics as well as machine learning algorithms. We aim to detect relevant participant subgroups by data-driven cluster algorithms and their impact on the subjects' long-term prognosis. The POKAL-PSY study is a subproject of the research network POKAL (Predictors and Clinical Outcomes in Depressive Disorders; GRK 2621).

Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life

BACKGROUND

Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

METHODS

Participants were 389 infants born before 32 weeks of gestation for whom bedside monitor data were available. A total of 29 infants had IVH grade 3 or 4 and 31 infants died within 2 weeks of life. SE was calculated with the PhysioNet open-source benchmark. Logistic regressions assessed associations between SE and IVH and/or mortality with and without common clinical covariates over various hour of life (HOL) censor points.

RESULTS

Lower SE was associated with mortality by 4 HOL, but higher SE was very strongly associated with IVH and mortality at 24-96 HOL. Bootstrap testing confirmed SE significantly improved prediction using clinical variables at 96 HOL.

CONCLUSION

SE is a significant predictor of IVH and mortality in premature infants. Given IVH typically occurs in the first 24-72 HOL, affected infants may initially have low SE followed by a sustained period of high SE.

IMPACT

SE correlates with IVH and mortality in preterm infants early in life. SE combined with clinical factors yielded ROC AUCs well above 0.8 and significantly outperformed the clinical model at 96 h of life. Previous studies had not shown predictive power over clinical models. First study using the PhysioNet Cardiovascular Toolbox benchmark in young infants. Relative to the generally accepted timing of IVH in premature infants, we saw lower SE before or around the time of hemorrhage and a sustained period of higher SE after. Higher SE after acute events has not been reported previously.

Longitudinal effects of early exposure to intermittent hypoxia on autonomic cardiovascular control in very preterm infants

INTRODUCTION

Cardiorespiratory control is immature in infants born preterm compared to those born at term. Animal studies have shown that repetitive hypoxia associated with periodic breathing can alter autonomic control. We aimed to elucidate if the amount of time spent with apnoea and periodic breathing in the neonatal unit was associated with longitudinal changes in autonomic control assessed using heart rate variability.

METHODS

Twenty-nine very preterm infants (10 M 19F) were studied during supine daytime sleep on 4 occasions. Study 1: 32-36 weeks post menstrual age (PMA) (n = 29), Study 2: 36-40 weeks PMA (n = 27), Study 3: 3-months corrected age (CA) (n = 20) and Study 4: 6-months CA (n = 26). The percentage total sleep time (%TST) spent having apnoeas in active (AS) and quiet sleep (QS) at each study was calculated. Total power, low frequency (LF, sympathetic + parasympathetic activity) high frequency (HF, parasympathetic activity), and LF/HF (sympathovagal balance) were calculated. Infants were divided into two groups based on the %TST spent with apnoeas above and below the median in AS and QS at Study 1. Data were normalised and compared with two-way ANOVA with Bonferroni post-hoc tests.

RESULTS

When apnoeas were included in the analysis, in QS Total power and HF power were higher, and when apnoeas were excluded HF power was higher in QS but lower in AS in the above median group at Study 4.

CONCLUSION

This study provides new evidence that short apnoeas, particularly periodic breathing, which is currently not detected or treated in the neonatal unit can affect autonomic cardiovascular control.

The effectiveness of a standardized tobacco cessation program on psychophysiological parameters in patients with addiction undergoing long-term rehabilitation: a quasi-experimental pilot study

BACKGROUND

Although there is a very high comorbidity between tobacco dependence and other addictive disorders, there are only few studies examining the implementation and outcomes of a tobacco cessation program in patients with addictive diseases. Therefore, the aim of this study is to investigate to what extent a standardized tobacco cessation program leads to improvements regarding psychological/physical parameters in patients with addiction undergoing therapy and whether there is a reduction in tobacco consumption.

METHODS

The study took place in a therapeutic community specialized in addiction therapy. A total sample of 56 participants were non-randomly assigned to an intervention group (IG; n = 31) and a treatment as usual group (TAUG; n = 25). The IG participated in a 6-week tobacco cessation program, while the TAUG received no additional treatment. Both groups were assessed for changes in primary outcomes (tobacco dependence, smoked cigarettes per day (CPD), and general substance-related craving) and secondary outcomes (heart rate variability (HRV): root mean square of successive differences, self-efficacy, and comorbid psychiatric symptoms) at two measurement time points (pre- and post-treatment/6 weeks).

RESULTS

We observed significant improvements in self-efficacy (F(1,53) = 5.86; p < .05; ηp2 = .11) and decreased CPD in the IG (β = 1.16, ρ < .05), while no significant changes were observed in the TAUG. No significant interaction effects were observed in psychiatric symptoms, general substance-related craving, and HRV.

CONCLUSIONS

The results highlight the potential benefit of an additional tobacco cessation program as part of a general addiction treatment. Although no improvements in the physiological domain were observed, there were significant improvements regarding self-efficacy and CPD in the IG compared to the TAUG. Randomized controlled trials on larger samples would be an important next step.

TRIAL REGISTRATION

ISRCTN15684371.

Promoting stress and anxiety recovery in older adults: assessing the therapeutic influence of biophilic green walls and outdoor view

Previous research has already provided evidence regarding the favorable impact of green walls and outdoor views on stress reduction and anxiety alleviation. However, there has been limited exploration into the combined effects of green walls and outdoor views on older adults. In this study, a between-subjects experiment was conducted using virtual reality (VR) technology with 23 participants. Following exposure to stressors, each participant underwent four randomized sessions, each lasting 5 min, in various virtual reality (VR) environments, encompassing non-biophilic and biophilic environments (including green walls, outdoor views, and their combination). Throughout the process, we measured physiological indicators of stress responses, including heart rate, heart rate variability, skin conductance levels, and blood pressure, using biometric sensors. Psychological changes in participants, including anxiety levels, were evaluated through the State-Trait Anxiety Inventory, recovery scales, and self-reported emotional assessments. In conclusion, in comparison to non-biophilic environments, older adults consistently exhibited lower stress levels, experienced superior anxiety relief, and demonstrated improved recovery in nature connectedness environments, with a notably faster recovery rate. These findings suggest that the incorporation of nature connectedness principles into the indoor environments of public activity spaces within older adults care facilities can significantly contribute to stress reduction and anxiety alleviation among older adults. Furthermore, these effects appear to be contingent on the specific types of nature connectedness environments. These results can provide substantial evidence to support the design of indoor common activity spaces within older adults care facilities.

The Impact of Prenatal Alcohol Exposure on the Autonomic Nervous System and Cardiovascular System in Rats in a Sex-Specific Manner

BACKGROUND

Fetal Alcohol Spectrum Disorder (FASD) is a consequence of prenatal alcohol exposure (PAE) associated with a range of effects, including dysmorphic features, prenatal and/or postnatal growth problems, and neurodevelopmental difficulties. Despite advances in treatment methods, there are still gaps in knowledge that highlight the need for further research. The study investigates the effect of PAE on the autonomic system, including sex differences that may aid in early FASD diagnosis, which is essential for effective interventions.

METHODS

During gestational days 5 to 20, five pregnant female Wistar rats were orally administered either glucose or ethanol. After 22 days, 26 offspring were born and kept with their mothers for 21 days before being isolated. Electrocardiographic recordings were taken on the 29th and 64th day. Heart rate variability (HRV) parameters were collected, including heart rate (HR), standard deviation (SD), standard deviation of normal-to-normal intervals (SDNN), and the root mean square of successive differences between normal heartbeats (RMSSD). Additionally, a biochemical analysis of basic serum parameters was performed on day 68 of the study.

RESULTS

The study found that PAE had a significant impact on HRV. While electrolyte homeostasis remained mostly unaffected, sex differences were observed across various parameters in both control and PAE groups, highlighting the sex-specific effects of PAE. Specifically, the PAE group had lower mean heart rates, particularly among females, and higher SDNN and RMSSD values. Additionally, there was a shift towards parasympathetic activity and a reduction in heart rate entropy in the PAE group. Biochemical changes induced by PAE were also observed, including elevated levels of alanine transaminase (ALT) and aspartate aminotransferase (AST), especially in males, increased creatinine concentration in females, and alterations in lipid metabolism.

CONCLUSIONS

PAE negatively affects the development of the autonomic nervous system, resulting in decreased heart rate and altered sympathetic activity. PAE also induces cardiovascular abnormalities with sex-specific effects, highlighting a relationship between PAE consequences and sex. Elevated liver enzymes in the PAE group may indicate direct toxic effects, while increased creatinine levels, particularly in females, may suggest an influence on nephrogenesis and vascular function. The reduced potassium content may be linked to hypothalamus-pituitary-adrenal axis overactivity.

To what degree patient-reported symptoms of central sensitization, kinesiophobia, disability, sleep, and life quality associated with 24-h heart rate variability and actigraphy measurements?

OBJECTIVES

Chronic musculoskeletal pain is associated with decreased parasympathetic and increased sympathetic activity in the autonomic nervous system. The objective of this study was to determine the associations between objective measures of heart rate variability (a measure of autonomic nervous system function), actigraphy (a measure of activity and sleep quality), respiration rates, and subjective patient-reported outcome measures (PROMs) of central sensitization, kinesiophobia, disability, the effect of pain on sleep, and life quality.

METHODS

Thirty-eight study subjects were divided into two subgroups, including low symptoms of central sensitization (n = 18) and high symptoms of central sensitization (n = 20), based on patient-reported scores on the Central Sensitization Inventory (CSI). Heart rate variability (HRV) and actigraphy measurements were carried out simultaneously in 24 h measurement during wakefulness and sleep.

RESULTS

A decrease in HRV during the first 2 h of sleep was stronger in the low CSI subgroup compared to the high CSI subgroup. Otherwise, all other HRV and actigraphy parameters and subjective measures of central sensitization, disability, kinesiophobia, the effect of pain on sleep, and quality of life showed only little associations.

DISCUSSION

The high CSI subgroup reported significantly more severe symptoms of disability, kinesiophobia, sleep, and quality of life compared to the low CSI subgroup. However, there were only small and nonsignificant trend in increased sympathetic nervous system activity and poorer sleep quality on the high central sensitization subgroup. Moreover, very little differences in respiratory rates were found between the groups.

The effects of cold exposure (cold water immersion, whole- and partial- body cryostimulation) on cardiovascular and cardiac autonomic control responses in healthy individuals: A systematic review, meta-analysis and meta-regression

BACKGROUND

Cryostimulation and cold-water immersion (CWI) have recently gained widespread attention due to their association with changes in cardiovascular and cardiac autonomic control responses. Therefore, the aim of the present systematic review and meta-analysis was to identify the global impact of such cold exposures on cardiovascular and cardiac autonomic activity.

METHODS

Three databases (PubMed, Embase, Web-of-Science) were used. Studies were eligible for inclusion if they were conducted on healthy participants using cryostimulation and/or CWI. The outcomes included measurements of blood pressure (BP), heart rate (HR), and heart rate variability (HRV) indices: RR interval (RR), Root mean square of successive RR interval differences (RMSSD), low frequency band (LF), high frequency band (HF), and LF/HF ratio.

RESULTS

Among the 27 articles included in our systematic literature review, only 24 were incorporated into the meta-analysis. Our results reveal a significant increase in HRV indices: RMSSD (Standardized mean difference (SMD) = 0.61, p < 0.001), RR (SMD = 0.77, p < 0.001), and HF (SMD = 0.46, p < 0.001), as well as significantly reduced LF (SMD = -0.41, p < 0.001) and LF/HF ratio (SMD = -0.25, p < 0.01), which persisted up to 15 min following cold exposure. Significantly decreased heart rate (SMD = -0.16, p < 0.05), accompanied by slightly increased mean BP (SMD = 0.28, p < 0.001), was also observed. These results seem to depend on individual characteristics and the cooling techniques.

CONCLUSION

Our meta-analysis suggests that cryostimulation and/or CWI exposure enhance parasympathetic nervous activity. There is scarce scientific literature regarding the effect of individual characteristics on cold-induced physiological responses.

Evaluation of indicators of acute emotional states in dogs

A complete assessment of animal welfare requires not just an understanding of negative emotional states, such as fear and anxiety, but also of positive states, such as calmness and happiness. However, few studies have identified accurate and reliable indicators of positive emotional states in dogs. This study aimed to identify parameters that may serve as indicators of short-term emotional states in dogs. Using a cross-over design, 60 dogs living at a research facility were exposed to six different 10-min scenarios expected to elicit responses varying in emotional valence and arousal. A range of behavioural and physiological parameters were collected and their relationship to anticipated emotional valence and arousal was analysed using linear and logistic mixed models. Cortisol, adrenocorticotropic hormone, heart rate variability, panting, whining, and body shake all demonstrated significant differences based on arousal levels, but only within negative valence scenarios. Scores from a qualitative behavioural assessment (QBA) were associated with both emotional valence and arousal and were considered the best indicator of positive valence. Activity, ear temperature, and sitting were associated with positive high arousal, although this may have been influenced by differing levels of movement induced during these scenarios. Meanwhile, heart rate, secretory immunoglobulin A, standing and lying all showed similar changes associated with arousal for both positive and negative valence scenarios. This study provides a critical first step towards identifying evidence-based indicators of short-term emotional states in dogs, while highlighting considerations that should be made when employing these parameters, including the influence of coder bias, food provision, exercise, and external temperature. Overall, it is recommended future dog emotion and welfare research use a combination of parameters including indicators of both emotional valence and arousal.

StepTest4all: Improving the Prediction of Cardiovascular Capacity Assessment in Young Adults

Cardiovascular capacity, expressed as maximal oxygen uptake (VO2max), is a strong predictor of health and fitness and is considered a key measure of physiological function in the healthy adult population. The aim of this study was to investigate the influence of the physical activity levels (PAlevel) of participants in the StepTest4all (validated protocol for the estimation of VO2max in adults). The sample consisted of 69 participants, including 27 women (age 21.7 ± 3.6 years; body mass = 63.5 ± 14.8 kg; height = 1.64 ± 0.06 m; body mass index = 23.7 ± 5.3 kg/m2) and 42 men (aged 21.7 ± 3.4 years; body mass = 72.0 ± 7.3 kg; height = 1.77 ± 0.07 m; body mass index = 23.1 ± 2.1 kg/m2). The participants were assigned to one of the two groups: (i) the VO2max prediction group and (ii) the prediction model validation group. In the multiple linear regression, the following predictors of VO2max remained significant: sex (p < 0.001), physical activity level (p = 0.014), and HRR60 (p = 0.020). The prediction equation (R2 = 74.0%, SEE = 4.78) showed a close and strong relationship between the measurements and can be expressed as follows: VO2max = 17.105 + 0.260·(HRR60) + 8.563·(sex) + 4.097·(PAlevel), in which HRR60 is the magnitude of the HR decrease (bpm) in one minute immediately after stopping the step, and sex: men = 1, women = 0, and PAlevel is level 1 (low), level 2 (moderate), and level 3 (high). The StepTest4all was shown to be a suitable method for estimating cardiovascular capacity, expressed as VO2max, in young adults. Retaining PAlevel as a significant predictor allows us to better individualize the participants' VO2max.

Olfaction in the canine cognitive and emotional processes: From behavioral and neural viewpoints to measurement possibilities

Domestic dogs (Canis familiaris) have excellent olfactory processing capabilities that are utilized widely in human society e.g., working with customs, police, and army; their scent detection is also used in guarding, hunting, mold-sniffing, searching for missing people or animals, and facilitating the life of the disabled. Sniffing and searching for odors is a natural, species-typical behavior and essential for the dog's welfare. While taking advantage of this canine ability widely, we understand its foundations and implications quite poorly. We can improve animal welfare by better understanding their olfactory world. In this review, we outline the olfactory processing of dogs in the nervous system, summarize the current knowledge of scent detection and differentiation; the effect of odors on the dogs' cognitive and emotional processes and the dog-human bond; and consider the methodological advancements that could be developed further to aid in our understanding of the canine world of odors.

Reducing clustering of readouts in non-Cartesian cine magnetic resonance imaging

BACKGROUND

Non-Cartesian magnetic resonance imaging trajectories at golden angle increments have the advantage of allowing motion correction and gating using intermediate real-time reconstructions. However, when the acquired data are cardiac binned for cine imaging, trajectories can cluster together at certain heart rates (HR) causing image artifacts. Here, we demonstrate an approach to reduce clustering by inserting additional angular increments within the trajectory, and optimizing them while still allowing for intermediate reconstructions.

METHODS

Three acquisition models were simulated under constant and variable HR: golden angle (Mtrd), random additional angles (Mrnd), and optimized additional angles (Mopt). The standard deviations of trajectory angular differences (STAD) were compared through their interquartile ranges (IQR) and the Kolmogorov-Smirnov test (significance level: p = 0.05). Agreement between an image reconstructed with uniform sampling and images from Mtrd, Mrnd, and Mopt was analyzed using the structural similarity index measure (SSIM). Mtrd and Mopt were compared in three adults at high, low, and no HR variability.

RESULTS

STADs from Mtrd were significantly different (p < 0.05) from Mopt and Mrnd. STAD (IQR × 10-2 rad) showed that Mopt (0.5) and Mrnd (0.5) reduced clustering relative to Mtrd (1.9) at constant HR. For variable HR, Mopt (0.5) and Mrnd (0.5) outperformed Mtrd (0.9). The SSIM (IQR) showed that Mopt (0.011) produced the best image quality, followed by Mrnd (0.014), and Mtrd (0.030). Mopt outperformed Mtrd at reduced HR variability in in-vivo studies. At high HR variability, both models performed well.

CONCLUSION

This approach reduces clustering in k-space and improves image quality.

Yoga Pose Estimation Using Angle-Based Feature Extraction

OBJECTIVE

This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by the COVID-19 pandemic and the subsequent shift to virtual platforms for yoga instruction. This research aims to develop a mechanism for detecting correct yoga poses and providing real-time feedback through the application of computer vision and machine learning (ML) techniques.

METHODS AND PROCEDURES

This study utilized computer vision-based pose estimation methods to extract features and calculate yoga pose angles. A variety of models, including extremely randomized trees, logistic regression, random forest, gradient boosting, extreme gradient boosting, and deep neural networks, were trained and tested to classify yoga poses. Our study employed the Yoga-82 dataset, consisting of many yoga pose images downloaded from the web.

RESULTS

The results of this study show that the extremely randomized trees model outperformed the other models, achieving the highest prediction accuracy of 91% on the test dataset and 92% in a fivefold cross-validation experiment. Other models like random forest, gradient boosting, extreme gradient boosting, and deep neural networks achieved accuracies of 90%, 89%, 90%, and 85%, respectively, while logistic regression underperformed, having the lowest accuracy.

CONCLUSION

This research concludes that the extremely randomized trees model presents superior predictive power for yoga pose recognition. This suggests a valuable avenue for future exploration in this domain. Moreover, the approach has significant potential for implementation on low-powered smartphones with minimal latency, thereby enabling real-time feedback for users practicing yoga at home.

Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach

Background

The diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects' autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD.

Methods

The present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved.

Results

The trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model.

Conclusion

In a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.

Autonomic cardiovascular control is altered by intermittent hypoxia in preterm infants

AIM

Preterm infants frequently experience short apnoeas and periodic breathing. Animal studies have shown that repetitive hypoxia associated with periodic breathing can alter autonomic control. We aimed to elucidate if apnoea and periodic breathing were associated with changes in autonomic control assessed using heart rate variability, thus exacerbating the consequences of respiratory disturbance.

METHODS

Forty very preterm infants (15 M/25 F) were studied at 34.3 weeks post-menstrual age with daytime polysomnography. Total power, low frequency (LF, sympathetic+parasympathetic activity) high frequency (HF, parasympathetic activity) and LF/HF (sympathovagal balance) were calculated.

RESULTS

Infants were divided into those with above and below the median total sleep time spent with respiratory events: Active sleep (AS) 13%, Quiet sleep (QS) 10%. In AS, including respiratory events, Total power (p < 0.05) and HF power (p < 0.05) were higher in the above median group. During AS excluding respiratory events, Total power (p < 0.05) and HF power (p = 0.061) were higher and LF power (p < 0.01) and LF/HF (p < 0.05) were lower in the above median group. There were no differences in HRV parameters in QS.

CONCLUSION

This study provides new evidence that short apnoeas, particularly periodic breathing, which is currently not detected or treated in the neonatal unit can affect autonomic cardiovascular control.

Sex differences in associations of plasma metabolites with blood pressure and heart rate variability: The HELIUS study

BACKGROUND AND AIMS

Since plasma metabolites can modulate blood pressure (BP) and vary between men and women, we examined sex differences in plasma metabolite profiles associated with BP and sympathicovagal balance. Our secondary aim was to investigate associations between gut microbiota composition and plasma metabolites predictive of BP and heart rate variability (HRV).

METHODS

From the HELIUS cohort, we included 196 women and 173 men. Office systolic BP and diastolic BP were recorded, and heart rate variability (HRV) and baroreceptor sensitivity (BRS) were calculated using finger photoplethysmography. Plasma metabolomics was measured using untargeted LC-MS/MS. Gut microbiota composition was determined using 16S sequencing. We used machine learning models to predict BP and HRV from metabolite profiles, and to predict metabolite levels from gut microbiota composition.

RESULTS

In women, best predicting metabolites for systolic BP included dihomo-lineoylcarnitine, 4-hydroxyphenylacetateglutamine and vanillactate. In men, top predictors included sphingomyelins, N-formylmethionine and conjugated bile acids. Best predictors for HRV in men included phenylacetate and gentisate, which were associated with lower HRV in men but not in women. Several of these metabolites were associated with gut microbiota composition, including phenylacetate, multiple sphingomyelins and gentisate.

CONCLUSIONS

Plasma metabolite profiles are associated with BP in a sex-specific manner. Catecholamine derivatives were more important predictors for BP in women, while sphingomyelins were more important in men. Several metabolites were associated with gut microbiota composition, providing potential targets for intervention.

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

BACKGROUND

Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications.

OBJECTIVE

We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements.

METHODS

This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions.

RESULTS

Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%.

CONCLUSIONS

Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

Multifractal foundations of biomarker discovery for heart disease and stroke

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability

Introduction

Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV).

Methods

Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested.

Results and Discussion

In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R² > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices.

Assessment of autonomic nervous system dysfunction associated with peripheral neuropathies in the context of clinical neurophysiology practice

Peripheral neuropathies may involve the small diameter nerve fibers of the autonomic nervous system. In the presence of clinical signs compatible with dysautonomia, it is very difficult to affirm that these signs are really linked to an alteration in postganglionic autonomic innervation, and not to a lesion of the central nervous system or to a direct damage to the tissues and innervated organs. Also, in the context of the investigation of peripheral neuropathies, there is an interest in performing objective and quantitative assessment of distal autonomic innervation. The corresponding autonomic tests are mainly based on the exploration of sudomotor or vasomotor disorders of the limb extremities. In this article, we provide an overview of the various tests available for the study of the autonomic nervous system in clinical practice, including vasomotor reactivity tests, in particular based on laser Doppler techniques, and sudomotor tests, based on axon-reflexes produced by iontophoresis of cholinergic drugs or on the simpler measurement of electrochemical skin conductance by the Sudoscan® device.

Reduced heart rate variability in people with type 1 diabetes and elevated diabetes distress: Results from the longitudinal observational DIA-LINK1 study

AIMS

People with type 1 diabetes have a higher risk for cardiovascular disease (CVD). Reduced heart rate variability (HRV) is a clinical marker for CVD. In this observational study using continuous HRV measurement across 26 days, we investigated whether psychological stressors (diabetes distress, depressive symptoms) and glycaemic parameters (hypo- and hyperglycaemic exposure, glycaemic variability and HbA_1c ) are associated with lower HRV in people with type 1 diabetes.

METHODS

Data from the non-interventional prospective DIA-LINK1 study were analysed. At baseline, depressive symptoms and diabetes distress were assessed. Glucose values and HRV were recorded daily for 26 days using continuous glucose monitoring (CGM) and a wrist-worn health tracker respectively. Multilevel modelling with participant as nesting factor was used to analyse associations between day-to-day HRV and diabetes distress, depressive symptoms and CGM-derived parameters.

RESULTS

Data from 149 participants were analysed (age: 38.3 ± 13.1 years, HbA_1c : 8.6 ± 1.9%). Participants with elevated diabetes distress had a significantly lower HRV across the 26 days compared to participants without elevated distress (β = -0.28; p = 0.004). Elevated depressive symptoms were not significantly associated with HRV (β = -0.18; p = 0.074). Higher daily exposure to hyperglycaemia (β = -0.44; p = 0.044), higher average exposure to hypoglycaemia (β = -0.18; p = 0.042) and higher HbA_1c (β = -0.20; p = 0.018) were associated with reduced HRV across the 26 days. Sensitivity analysis with HRV averaged across all days corroborated these results.

CONCLUSIONS

Diabetes distress is a clinically meaningful psychosocial stressor that could play a role in the cardiovascular health of people with type 1 diabetes. These findings highlight the need for integrated psychosocial care in diabetes management.

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.

Effect of antipsychotic use by patients with schizophrenia on deceleration capacity and its relation to the corrected QT interval

OBJECTIVE

Schizophrenia patients treated with antipsychotics are at higher risk of sudden cardiac death. Decreased deceleration capacity (DC) of the heart rate is an accurate predictor of cardiac mortality. We evaluated the risk of sudden cardiac death due to antipsychotic use by assessing DC and examining the association between DC and the corrected QT interval (QTc) in schizophrenia patients.

METHODS

We measured the DC and QTc of 138 schizophrenia patients. We then compared the DC of 86 age- and sex-matched healthy controls with that of 86 schizophrenia patients. We investigated the correlation of DC of approximately 138 schizophrenia patients with prescribed doses of antipsychotics using linear regression analysis. We compared the DC of schizophrenia patients with and without prolonged QT intervals.

RESULTS

We found DC significantly differed between schizophrenia patients on antipsychotic medication and healthy controls. Additionally, DC was negatively correlated with antipsychotic use, especially chlorpromazine, zotepine, olanzapine and clozapine, in a dose-dependent manner. There was no significant association between DC and the QTc.

CONCLUSION

Assessing DC could facilitate monitoring and identification of increased risk of cardiac mortality in patients with schizophrenia that take antipsychotics. Assessing both DC and the QTc may enhance the accuracy of predicting sudden cardiac death.

The Role of Heart Rate Variability (HRV) in Different Hypertensive Syndromes

Cardiac innervation by the parasympathetic nervous system (PNS) and the sympathetic nervous system (SNS) modulates the heart rate (HR) (chronotropic activity) and the contraction of the cardiac muscle (inotropic activity). The peripheral vasculature is controlled only by the SNS, which is responsible for peripheral vascular resistance. This also mediates the baroreceptor reflex (BR), which in turn mediates blood pressure (BP). Hypertension (HTN) and the autonomic nervous system (ANS) are closely related, such that derangements can lead to vasomotor impairments and several comorbidities, including obesity, hypertension, resistant hypertension, and chronic kidney disease. Autonomic dysfunction is also associated with functional and structural changes in target organs (heart, brain, kidneys, and blood vessels), increasing cardiovascular risk. Heart rate variability (HRV) is a method of assessing cardiac autonomic modulation. This tool has been used for clinical evaluation and to address the effect of therapeutic interventions. The present review aims (a) to approach the heart rate (HR) as a CV risk factor in hypertensive patients; (b) to analyze the heart rate variability (HRV) as a "tool" to estimate the individual risk stratum for Pre-HTN (P-HTN), Controlled-HTN (C-HTN), Resistant and Refractory HTN (R-HTN and Rf-HTN, respectively), and hypertensive patients with chronic renal disease (HTN+CKD).

Machine learning in biosignals processing for mental health: A narrative review

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.

Automatic detection of the mental state in responses towards relaxation

Nowadays, considering society's highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of 25.76 ± 3.7 years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to 94.01 ± 1.73 % with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of 90.36 ± 1.62 %.

Chronic maternal exposure to titanium dioxide nanoparticles alters breathing in newborn offspring

Background

Over the last two decades, nanotechnologies and the use of nanoparticles represent one of the greatest technological advances in many fields of human activity. Particles of titanium dioxide (TiO₂) are one of the nanomaterials most frequently found in everyday consumer products. But, due in particular to their extremely small size, TiO₂ nanoparticles (NPs) are prone to cross biological barriers and potentially lead to adverse health effects. The presence of TiO₂ NPs found in human placentae and in the infant meconium has indicated unequivocally the capacity for a materno-fetal transfer of this nanomaterial. Although chronic exposure to TiO₂ NPs during pregnancy is known to induce offspring cognitive deficits associated with neurotoxicity, the impact of a gestational exposure on a vital motor function such as respiration, whose functional emergence occurs during fetal development, remains unknown.

Results

Using in vivo whole-body plethysmographic recordings from neonatal mice, we show that a chronic exposure to TiO₂ NPs during pregnancy alters the respiratory activity of offspring, characterized by an abnormally elevated rate of breathing. Correspondingly, using ex vivo electrophysiological recordings performed on isolated brainstem-spinal cord preparations of newborn mice and medullary slice preparations containing specific nuclei controlling breathing frequency, we show that the spontaneously generated respiratory-related rhythm is significantly and abnormally accelerated in animals prenatally exposed to TiO₂ NPs. Moreover, such a chronic prenatal exposure was found to impair the capacity of respiratory neural circuitry to effectively adjust breathing rates in response to excitatory environmental stimuli such as an increase in ambient temperature.

Conclusions

Our findings thus demonstrate that a maternal exposure to TiO₂ NPs during pregnancy affects the normal development and operation of the respiratory centers in progeny.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00497-4.

Objective Measurement of Learners' Cognitive Load During Simulation-Based Trauma Team Training: A Pilot Study

INTRODUCTION

Literature has shown cognitive overload which can negatively impact learning and clinical performance in surgery. We investigated learners' cognitive load during simulation-based trauma team training using an objective digital biomarker.

METHODS

A cross-sectional study was carried out in a simulation center where a 3-h simulation-based interprofessional trauma team training program was conducted. A session included three scenarios each followed by a debriefing session. One scenario involved multiple patients. Learners wore a heart rate sensor that detects interbeat intervals in real-time. Low-frequency/high-frequency (LF/HF) ratio was used as a validated proxy for cognitive load. Learners' LF/HF ratio was tracked through different phases of simulation.

RESULTS

Ten subjects participated in 12 simulations. LF/HF ratios during scenario versus debriefing were compared for each simulation. These were 3.75 versus 2.40, P < 0.001 for scenario 1; 4.18 versus 2.77, P < 0.001 for scenario 2; and 4.79 versus 2.68, P < 0.001 for scenario 3. Compared to single-patient scenarios, multiple-patient scenarios posed a higher cognitive load, with LF/HF ratios of 3.88 and 4.79, P < 0.001, respectively.

CONCLUSIONS

LF/HF ratio, a proxy for cognitive load, was increased during all three scenarios compared to debriefings and reached the highest levels in a multiple-patient scenario. Using heart rate variability as an objective marker of cognitive load is feasible and this metric is able to detect cognitive load fluctuations during different simulation phases and varying scenario difficulties.

Multi-granular Analysis and Physiological Interpretations of Heart Rate Variability Metrics During VR-Shooting Difficulty Induced Stress

Physiological sensing of virtual reality (VR)-induced stressors are increasingly utilized to improve human training and assess the impact of gaming difficulty-induced stress on a person's health and well-being. However, the prior art sparsely explores the multi-level cardiovascular dynamics for psychophysiological demands in a VR environment. This treatise discusses the experimental findings and physiological interpretations of various heart rate variability (HRV) metrics extracted from 31 participants during a Go/No-Go VR-based shooting task across multiple timeframes. The VR-shooting exercise consists of firing at the enemy targets while sparing the friendly ones for different shooting difficulty levels: low-difficulty and high-difficulty with in-between baselines. Ex-perimental results demonstrate consistent shooting difficulty-induced stress patterns at multi-granular levels in response to the heterogeneous inputs (exogenous and endogenous factors). The physiological interpretations highlight the intricate inter-play between cardio-physiological components: sympathetic and parasympathetic response across multiple timescales (sessions and blocks) and shooting difficulty levels.

Associations between fetal heart rate variability and umbilical cord occlusions-induced neural injury: An experimental study in a fetal sheep model

INTRODUCTION

This study evaluated the association between fetal heart rate variability (HRV) and the occurrence of hypoxic-ischemic encephalopathy in a fetal sheep model.

MATERIAL AND METHODS

The experimental protocol created a hypoxic condition with repeated cord occlusions in three phases (A, B, C) to achieve acidosis to pH <7.00. Hemodynamic, gasometric and HRV parameters were analyzed during the protocol, and the fetal brain, brainstem and spinal cord were assessed histopathologically 48 h later. Associations between the various parameters and neural injury were compared between phases A, B and C using Spearman's rho test.

RESULTS

Acute anoxic-ischemic brain lesions in all regions was present in 7/9 fetuses, and specific neural injury was observed in 3/9 fetuses. The number of brainstem lesions correlated significantly and inversely with the HRV fetal stress index (r = -0.784; p = 0.021) in phase C and with HRV long-term variability (r = -0.677; p = 0.045) and short-term variability (r = -0.837; p = 0.005) in phase B. The number of neurological lesions did not correlate significantly with other markers of HRV.

CONCLUSIONS

Neural injury caused by severe hypoxia was associated with HRV changes; in particular, brainstem damage was associated with changes in fetal-specific HRV markers.

The Mechanism of Cardiac Sympathetic Activity Assessment Methods: Current Knowledge

This review has summarized the methods currently available for cardiac sympathetic assessment in clinical or under research, with emphasis on the principles behind these methodologies. Heart rate variability (HRV) and other methods based on heart rate pattern analysis can reflect the dominance of sympathetic nerve to sinoatrial node function and indirectly show the average activity level of cardiac sympathetic nerve in a period of time. Sympathetic neurotransmitters play a key role of signal transduction after sympathetic nerve discharges. Plasma or local sympathetic neurotransmitter detection can mediately display sympathetic nerve activity. Given cardiac sympathetic nerve innervation, i.e., the distribution of stellate ganglion and its nerve fibers, stellate ganglion activity can be recorded either directly or subcutaneously, or through the surface of the skin using a neurophysiological approach. Stellate ganglion nerve activity (SGNA), subcutaneous nerve activity (SCNA), and skin sympathetic nerve activity (SKNA) can reflect immediate stellate ganglion discharge activity, i.e., cardiac sympathetic nerve activity. These cardiac sympathetic activity assessment methods are all based on the anatomy and physiology of the heart, especially the sympathetic innervation and the sympathetic regulation of the heart. Technological advances, discipline overlapping, and more understanding of the sympathetic innervation and sympathetic regulation of the heart will promote the development of cardiac sympathetic activity assessment methods.

Dynamic Phase Extraction: Applications in Pulse Rate Variability

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

Involvement of the Renin-Angiotensin System in Stress: State of the Art and Research Perspectives

BACKGROUND

Along with other canonical systems, the renin-angiotensin system (RAS) has shown important roles in stress. This system is a complex regulatory proteolytic cascade composed of various enzymes, peptides, and receptors. Besides the classical (ACE/Ang II/AT1 receptor) and the counter-regulatory (ACE2/Ang-(1-7)/Mas receptor) RAS axes, evidence indicates that nonclassical components, including Ang III, Ang IV, AT₂ and AT₄, can also be involved in stress.

OBJECTIVE AND METHODS

This comprehensive review summarizes the current knowledge on the participation of RAS components in different adverse environmental stimuli stressors, including air jet stress, cage switch stress, restraint stress, chronic unpredictable stress, neonatal isolation stress, and post-traumatic stress disorder.

RESULTS AND CONCLUSION

In general, activation of the classical RAS axis potentiates stress-related cardiovascular, endocrine, and behavioral responses, while the stimulation of the counter-regulatory axis attenuates these effects. Pharmacological modulation in both axes is optimistic, offering promising perspectives for stress-related disorders treatment. In this regard, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers are potential candidates already available since they block the classical axis, activate the counter-regulatory axis, and are safe and efficient drugs.

Music Modulates Autonomic Nervous System Activity in Human Fetuses

Context

Fetal Autonomic Nervous sysTem Evaluation (FANTE) is a non-invasive tool that evaluates the autonomic nervous system activity in a fetus. Autonomic nervous system maturation and development during prenatal life are pivotal for the survival and neuropsychiatric development of the baby.

Objective

Aim of the study is to evaluate the effect of music stimulation on fetal heart rate and specific parameters linked to ANS activity, in particular fetal heart rate variability.

Methods

Thirty-two women between the 32nd and 38th week with a singleton uncomplicated pregnancy were recruited. All FANTE data collections were acquired using a 10-derivation electrocardiograph placed on the maternal abdomen. In each session (5 min basal, 10 min with music stimulus, and 5 min post-stimulus), FANTE was registered. The music stimulus was "Clair de lune" Debussy, played through headphones on the mother's abdomen (CTR: 31927).

Results

Music does not change the mean value of fetal heart rate. However, indices of total fetal heart rate variability statistically increase (RRsd p = 0.037, ANNsd p = 0.039, SD2 p = 0.019) during music stimulation in comparison to the basal phase. Heart rate variability increase depends mainly on the activation of parasympathetic branches (CVI p = 0.013), meanwhile, no significant changes from basal to stimulation phase were observed for indices of sympathetic activity. All the parameters of heart rate variability and parasympathetic activity remained activated in the post-stimulus phase compared to the stimulus phase. In the post-stimulus phase, sympathetic activity resulted in a significant reduction (LFn p = 0.037).

Conclusion

Music can influence the basal activity of the fetal autonomic nervous system, enhancing heart rate variability, without changing fetal heart rate mean value. Music is enabled to induce a relaxation state in a near-to-term fetus, mediated by parasympathetic activation and by a parallel sympathetic inhibition.

Early life stress and autonomic response to acute mental stress in individuals with coronary heart disease

Early life stress (ELS) has been associated with an increased risk of cardiovascular disease. We examined whether ELS was associated with autonomic function and stress reactivity among individuals with coronary heart disease (CHD). We included patients with stable CHD from two parallel studies, the Mental Stress Ischemia Prognosis Study (MIPS) and the Myocardial Infarction and Mental Stress Study 2 (MIMS2), and assessed ELS using the Early Trauma Inventory-Self-Report-Short Form. Participants underwent a laboratory-based mental stress task while undergoing ambulatory electrocardiographic monitoring. We used multivariate linear regression models to estimate the associations between ELS and heart rate variability (HRV; low frequency [LF], high frequency [HF], and LF and HF [LH] ratio). The analytic sample included 405 MIPS and 284 MIMS2 participants. Most participants endorsed at least one experience of ELS (92.2%). Although we did not observe associations between ELS and HRV outcomes in the overall sample, ELS was associated with lower LH ratio HRV during recovery in the posttraumatic stress disorder (PTSD) subgroup, ELS x PTSD interaction, p = .041. In the MIMS2 subgroup, ELS was associated with lower resting period LF HRV,B ̂ $ \widehat{B} $  = -0.16 ln ms2 ; 95% CI [-0.31, -0.02]. Exposure to physical trauma was associated with decreased HF HRV overall reactivity only among participants with high to moderate depressive symptoms,B ̂ $ \widehat{B} $  = -0.52 ln ms2 vs.B ̂ $ \widehat{B} $  = 0.01 ln ms2 , p = .013. Overall, heterogeneous associations between ELS and HRV emerged, suggesting the need for additional research regarding longer-term ambulatory HRV.

Career Interview Readiness in Virtual Reality (CIRVR): A Platform for Simulated Interview Training for Autistic Individuals and Their Employers

Employment outcomes for autistic 1 individuals are often poorer relative to their neurotypical (NT) peers, resulting in a greater need for other forms of financial and social support. While a great deal of work has focused on developing interventions for autistic children, relatively less attention has been paid to directly addressing the employment challenges faced by autistic adults. One key impediment to autistic individuals securing employment is the job interview. Autistic individuals often experience anxiety in interview situations, particularly with open-ended questions and unexpected interruptions. They also exhibit atypical gaze patterns that may be perceived as, but not necessarily indicative of, disinterest or inattention. In response, we developed a closed-loop adaptive virtual reality (VR)–based job interview training platform, which we have named Career Interview Readiness in VR (CIRVR). CIRVR is designed to provide an engaging, adaptive, and individualized experience to practice and refine interviewing skills in a less anxiety-inducing virtual context. CIRVR contains a real-time physiology-based stress detection module, as well as a real-time gaze detection module, to permit individualized adaptation. We also present the first prototype of the CIRVR Dashboard, which provides visualizations of data to help autistic individuals as well as potential employers and job coaches make sense of the data gathered from interview sessions. We conducted a feasibility study with 9 autistic and 8 NT individuals to assess the preliminary usability and feasibility of CIRVR. Results showed differences in perceived usability of the system between autistic and NT participants, and higher levels of stress in autistic individuals during interviews. Participants across both groups reported satisfaction with CIRVR and the structure of the interview. These findings and feedback will support future work in improving CIRVR’s features in hopes for it to be a valuable tool to support autistic job candidates as well as their potential employers.

The Slowest Shared Resonance: A Review of Electromagnetic Field Oscillations Between Central and Peripheral Nervous Systems

Electromagnetic field oscillations produced by the brain are increasingly being viewed as causal drivers of consciousness. Recent research has highlighted the importance of the body's various endogenous rhythms in organizing these brain-generated fields through various types of entrainment. We expand this approach by examining evidence of extracerebral shared oscillations between the brain and other parts of the body, in both humans and animals. We then examine the degree to which these data support one of General Resonance Theory's (GRT) principles: the Slowest Shared Resonance (SSR) principle, which states that the combination of micro- to macro-consciousness in coupled field systems is a function of the slowest common denominator frequency or resonance. This principle may be utilized to develop a spatiotemporal hierarchy of brain-body shared resonance systems. It is predicted that a system's SSR decreases with distance between the brain and various resonating structures in the body. The various resonance relationships examined, including between the brain and gastric neurons, brain and sensory organs, and brain and spinal cord, generally match the predicted SSR relationships, empirically supporting this principle of GRT.

The association between chronotype and sleep quality among female home care workers performing shift work

There is a scarcity of evidence on the association between shift work, sleeping parameters, heart rate variability (HRV), and chronotype, i.e., morningness and eveningness. The aims of this study were to 1) compare participants with different chronotypes (morning (M), evening (E), or neither (N)) in terms of their total sleep time, sleep efficiency, and HRV parameters, taking their age into account, and 2) examine whether self-reported work-related stress, the length of the working career and years performing shift work affect this association. The participants of the study were home care workers working in two shifts in one municipality in Eastern Finland (N = 395). Of these, 52 females (mean age 42.78 y, SD 12.92 y) completed the study questionnaire and participated in physiological measurements. Several sleep-related parameters were assessed (total sleep time, sleep efficiency, number of awakenings, and length of awakening) and indices of autonomic nervous system based on HRV were calculated. The participants worked in two shifts: a morning shift (7:00-15:00 h) and an evening shift (14:00-21:30 h). All these parameters were assessed during the night before the first work shift (N1), the night before the second work shift (N2), the night before the final work shift (N3), and the night before the first day off work (N4). According to the results, 21.2% of the participants were M-types, 17.3% were E-types, and 61.5% were N-types. On average, the participants had been in working life for 18.8 years and performing shift work for 13.7 years. On night N3, E-types had a significantly shorter total sleep time and spent less time in bed compared to M- and N-types. The total sleep time of M-type and N-type participants was on average 66 minutes and 82 minutes longer, respectively, when compared to E-types on night N3. There were no statistically significant differences in actigraphy-based sleep quality parameters between M-, N-, and E-types on nights N1, N2, and N4. Our results together indicate that M- and N-type individuals may have better sleep quality than E-types, which was also reflected in HRV parameters. Further research with longitudinal study design and workplace interventions is needed to determine how the chronotype can be optimally and individually utilized to improve the health and well-being of morning-type and evening-type people. This is particularly important for both younger and older workers entering the workforce to support healthier and longer working lives.

Influence of Spontaneous and Mechanical Ventilation on Frequency-Based Measures of Heart Rate Variability

Frequency-based measures of heart rate variability have been shown to be a useful physiological marker in both clinical and research settings providing insight into the functioning of the autonomic nervous system. Ongoing interactions between the autonomic nervous system control of the heart and lung occurs during each ventilation cycle because of their anatomical position within the closed thoracic cavity. Mechanical ventilation and subsequent removal change the normal ventilator mechanics producing alterations in the tidal volume, intrathoracic pressure, and oxygen delivery. A noninvasive method called heart rate variability (HRV) can be used to evaluate this interaction during ventilation and can be quantified by applying frequency-based measures of the variability between heartbeats. Although HRV is a reliable method to measure alteration of the autonomic nervous system (ANS) function and cardiopulmonary interaction, there have been limited reports concerning the changes in the frequency-based measure of HRV during both spontaneous and mechanical ventilation. The purpose of this methodological study is therefore to describe the physiological influence of both spontaneous and mechanical ventilation on frequency-based measures of HRV.

Exploring the Effects of Yoga Therapy on Heart Rate Variability and Patient-Reported Outcomes After Cancer Treatment: A Study Protocol

BACKGROUND

Following cancer treatment, adults commonly report worsened patient-reported outcomes (PROs) such as anxiety, stress, depression, persistent and upsetting cognitive complaints, unrelenting fatigue, and reduced quality of life. Poorer PROs are associated with disrupted autonomic nervous system functioning as measured by heart rate variability (HRV), both of which have been associated with greater morbidity and mortality. Interventions to improve HRV and PROs among adults following cancer treatment are needed. Yoga therapy holds promise as an intervention to improve HRV and PROs. Therefore, we conducted a single-subject exploratory experimental study to investigate the effects of yoga therapy on HRV and specific PROs (ie, cancer-related fatigue, anxiety, cognitive function, depression, stress, quality of life) in adults treated for cancer. To reduce publication bias, improve reproducibility, and serve as a reference for forthcoming reporting of study results, we present the study protocol for this study herein.

METHODS

Participants were adults who completed cancer treatment that were recruited from the Ottawa Integrative Cancer Centre. Consenting and eligible participants received one 1:1 yoga therapy session (ie, 1 participant, 1 Yoga Therapist) and 6 weekly group-based yoga therapy sessions (ie, 2-3 participants, 1 Yoga Therapist). Participants completed assessments 7 times: 3 times prior to the program (ie, -6 weeks, -3 weeks, immediately prior to the 1:1 yoga therapy session), immediately following the 1:1 yoga therapy session, prior to the first group-based yoga therapy session, after the last group-based yoga therapy session, and at a 6-week follow-up. Hierarchical linear modeling will be used to test the average effects of the yoga therapy program across participants.

DISCUSSION

This study will explore several novel hypotheses, including whether yoga therapy can improve HRV and/or specific PROs among adults treated for cancer acutely (ie, during a 1:1 yoga therapy session) and/or through repeated exposure (ie, after completing 6 weeks of group-based yoga therapy). Although the findings will require confirmation or refutation in future trials, they may provide initial evidence that YT may benefit adults treated for cancer.

TRIAL REGISTRATION

ISRCTN registry, ISRCTN64763228. Registered on December 12, 2021. This trial was registered retrospectively. URL of trial registry record: https://www.isrctn.com/ISRCTN64763228.

Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure

Congestive heart failure (CHF) is a chronic cardiovascular condition associated with dysfunction of the autonomic nervous system (ANS). Heart rate variability (HRV) has been widely used to assess ANS. This paper proposes a new HRV analysis method, which uses information-based similarity (IBS) transformation and fuzzy approximate entropy (fApEn) algorithm to obtain the fApEn_IBS index, which is used to observe the complexity of autonomic fluctuations in CHF within 24 h. We used 98 ECG records (54 health records and 44 CHF records) from the PhysioNet database. The fApEn_IBS index was statistically significant between the control and CHF groups (p < 0.001). Compared with the classical indices low-to-high frequency power ratio (LF/HF) and IBS, the fApEn_IBS index further utilizes the changes in the rhythm of heart rate (HR) fluctuations between RR intervals to fully extract relevant information between adjacent time intervals and significantly improves the performance of CHF screening. The CHF classification accuracy of fApEn_IBS was 84.69%, higher than LF/HF (77.55%) and IBS (83.67%). Moreover, the combination of IBS, fApEn_IBS, and LF/HF reached the highest CHF screening accuracy (98.98%) with the random forest (RF) classifier, indicating that the IBS and LF/HF had good complementarity. Therefore, fApEn_IBS effusively reflects the complexity of autonomic nerves in CHF and is a valuable CHF assessment tool.