Cardiorespiratory dynamic response to mental stress: a multivariate time-frequency analysis

Stress, psychiatric disease, and obesity: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022

Background

Previous Obesity Medicine Association (OMA) Clinical Practice Statements (CPS) included topics such as behavior modification, motivational interviewing, and eating disorders, as well as the effect of concomitant medications on weight gain/reduction (i.e., including psychiatric medications). This OMA CPS provides clinicians a more focused overview of stress and psychiatric disease as they relate to obesity.

Methods

The scientific support for this CPS is based upon published citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership.

Results

Topics in this CPS include the relationship between psychological stress and obesity, including both acute and chronic stress. Additionally, this CPS describes the neurobiological pathways regarding stress and addiction-like eating behavior and explores the relationship between psychiatric disease and obesity, with an overview of psychiatric medications and their potential effects on weight gain and weight reduction.

Conclusions

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on stress and psychiatric disease is one of a series of OMA CPSs designed to assist clinicians in the care of patients with the disease of obesity. Knowledge of stress, addiction-like eating behavior, psychiatric disease, and effects of psychiatric medications on body weight may improve the care obesity medicine clinicians provide to their patients with obesity.

The association of work-related extended availability with recuperation, well-being, life domain balance and work: A meta-analysis

Work-related extended availability (WREA; the availability of employees for work-related matters in their leisure time) seems to be associated with decreases in well-being and life-domain balance, but to date there is no quantitative synthesis of the scattered evidence. We conducted a random-effects meta-analysis (113 articles with 121 studies relying on k = 117 independent samples with N = 131,872) on the associations between WREA and employee outcomes while examining potential moderators as well as differences between availability demands and behaviors. WREA was adversely associated with recuperation, well-being and private life, but favorably with some work-related criteria. There were no systematic differences in effect sizes between availability demands and behaviors; however, segmentation preferences were a moderator. Overall, these results suggest that WREA may pose a threat to employee recuperation, well-being and private lives, especially when employees prefer separating work and private life. However, positive potentials of WREA should not be overlooked. Plain Language Summary Work-related extended availability (WREA) refers to the availability of employees for work-related matters in their leisure time. Studies have shown that WREA may go along with primarily negative consequences for employees, but to date, there is no comprehensive overview of the literature statistically summarizing the current state of research, which was done in the study at hand. We assumed that WREA be related to problems with recovery, poorer well-being and difficulties to find a balance between work and private life. We also assessed in how far WREA goes along with attitudes towards work, absence from work and the intention to change jobs. Moreover, we considered differences between demands to be available and behaviors of actually taking care of work-related matters during leisure time. Finally, we investigated factors that may be associated with stronger consequences of WREA. We included 113 scientific papers with a total of 131,872 participants. WREA was related to problems with recovery, poorer well-being and difficulties to find a balance between work and private life, but also to more positive attitudes towards work. We did not find systematic differences between demands to be available and availability behaviors. However, we found that the relationship between WREA and work creating conflict with family life were stronger in samples with higher preferences to segment work and private life. Our findings suggest that WREA may pose a threat to employee recuperation, well-being and private lives, especially when employees prefer separating life domains. Still, positive potentials of WREA should not be overlooked.

Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular control

OBJECTIVE

In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations.

APPROACH

We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and synergistic contributions, is obtained using a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This novel approach allows to quantify the directed information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations among the analyzed processes. Additionally, it provides analytical expressions for the computation of the information measures, by exploiting the theory of state space models. The approach is first illustrated in simulated VARFI processes and then applied to H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress.

MAIN RESULTS

We demonstrate the ability of the VARFI modeling approach to account for the coexistence of short-term and long-range correlations in the study of multivariate processes. Physiologically, we show that postural stress induces larger redundant and synergistic effects from S and R to H at short time scales, while mental stress induces larger information transfer from S to H at longer time scales, thus evidencing the different nature of the two stressors.

SIGNIFICANCE

The proposed methodology allows to extract useful information about the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems, which cannot be observed using standard methods that do not consider long-range correlations.

Odor perception and symptoms during acrolein exposure in individuals with and without building-related symptoms

Building-related symptoms (BRS) is a significant work-related and public health problem, characterized by non-specific symptoms occurring in a particular building. The cause of BRS is unknown, but certain reactive compounds are suggested risk factors. The aim of this controlled exposure study was to investigate whether BRS cases report more odor annoyance and symptoms and show altered autonomous nervous system (ANS) response during exposure to the reactive aldehyde, acrolein in comparison with referents. Individuals with BRS (n = 18) and referents (n = 14) took part in two exposure sessions (80 min). One session contained heptane alone, and the other heptane and acrolein. Perceived odor annoyance; eye, nose, and throat symptoms; and ANS response were measured continuously. BRS cases did not experience more odor annoyance; eye, nose, and throat symptoms; or altered ANS response in comparison with referents during the exposures. Supplementary analyses revealed that BRS cases that also reported chemical intolerance perceived more symptoms than referents during acrolein exposure. Acrolein exposure at a concentration below previously reported sensory irritation detection thresholds is perceived as more irritating by a subgroup of BRS individuals compared with referents. The results of this study indicate that a subset of individuals with building related symptoms (BRS) has a lowered sensory irritation threshold towards acrolein exposure. Future guidelines on chemical exposures to acrolein should take time and individual sensitivity into account.

Medically-oriented design for explainable AI for stress prediction from physiological measurements

Background

In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health.

Methods

In this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction.

Results

Test results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system.

Conclusion

In this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

Bispectral Index Alterations and Associations With Autonomic Changes During Hypnosis in Trauma Center Researchers: Formative Evaluation Study

BACKGROUND

Previous work performed by our group demonstrated that intermittent reductions in bispectral index (BIS) values were found during neurofeedback following mindfulness instructions. Hypnosis was induced to enhance reductions in BIS values.

OBJECTIVE

This study aims to assess physiologic relaxation and explore its associations with BIS values using autonomic monitoring.

METHODS

Each session consisted of reading a 4-minute baseline neutral script and playing an 18-minute hypnosis tape to 3 researchers involved in the BIS neurofeedback study. In addition to BIS monitoring, autonomic monitoring was performed, and this included measures of electromyography (EMG), skin temperature, skin conductance, respiratory rate, expired carbon dioxide, and heart rate variability. The resulting data were analyzed using two-tailed t tests, correlation analyses, and multivariate linear regression analyses.

RESULTS

We found that hypnosis was associated with reductions in BIS (P<.001), EMG (P<.001), respiratory rate (P<.001), skin conductance (P=.006), and very low frequency power (P=.04); it was also associated with increases in expired carbon dioxide (P<.001), skin temperature (P=.04), high frequency power (P<.001), and successive heart interbeat interval difference (P=.04) values. Decreased BIS values were associated with reduced EMG measures (R=0.76; P<.001), respiratory rate (R=0.35; P=.004), skin conductance (R=0.57; P<.001), and low frequency power (R=0.32; P=.01) and with increased high frequency power (R=-0.53; P<.001), successive heart interbeat interval difference (R=-0.32; P=.009), and heart interbeat interval SD (R=-0.26; P=.04) values.

CONCLUSIONS

Hypnosis appeared to induce mental and physical relaxation, enhance parasympathetic neural activation, and attenuate sympathetic nervous system activity, changes that were associated with BIS values. Findings from this preliminary formative evaluation suggest that the current hypnosis model may be useful for assessing autonomic physiological associations with changes in BIS values, thus motivating us to proceed with a larger investigation in trauma center nurses and physicians.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis

This study aims at investigating the development of premature infants' autonomic nervous system (ANS) based on a quantitative analysis of the heart-rate variability (HRV) with a variety of novel features. Additionally, the role of heart-rate drops, known as bradycardias, has been studied in relation to both clinical and novel sympathovagal indices. ECG data were measured for at least 3 h in 25 preterm infants (gestational age ≤32 weeks) for a total number of 74 recordings. The post-menstrual age (PMA) of each patient was estimated from the RR interval time-series by means of multivariate linear-mixed effects regression. The tachograms were segmented based on bradycardias in periods after, between and during bradycardias. For each of those epochs, a set of temporal, spectral and fractal indices were included in the regression model. The best performing model has R 2 = 0.75 and mean absolute error MAE = 1.56 weeks. Three main novelties can be reported. First, the obtained maturation models based on HRV have comparable performance to other development models. Second, the selected features for age estimation show a predominance of power and fractal features in the very-low- and low-frequency bands in explaining the infants' sympathovagal development from 27 PMA weeks until 40 PMA weeks. Third, bradycardias might disrupt the relationship between common temporal indices of the tachogram and the age of the infant and the interpretation of sympathovagal indices. This approach might provide a novel overview of post-natal autonomic maturation and an alternative development index to other electrophysiological data analysis.

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

Heart Rate Variability Synchronizes When Non-experts Vocalize Together

Singing and chanting are ubiquitous across World cultures. It has been theorized that such practices are an adaptive advantage for humans because they facilitate bonding and cohesion between group members. Investigations into the effects of singing together have so far focused on the physiological effects, such as the synchronization of heart rate variability (HRV), of experienced choir singers. Here, we study whether HRV synchronizes for pairs of non-experts in different vocalizing conditions. Using time-frequency coherence (TFC) analysis, we find that HRV becomes more coupled when people make long (> 10 s) sounds synchronously compared to short sounds (< 1 s) and baseline measurements (p < 0.01). Furthermore, we find that, although most of the effect can be attributed to respiratory sinus arrhythmia, some HRV synchronization persists when the effect of respiration is removed: long notes show higher partial TFC than baseline and breathing (p < 0.05). In addition, we observe that, for most dyads, the frequency of the vocalization onsets matches that of the peaks in the TFC spectra, even though these frequencies are above the typical range of 0.04–0.4 Hz. A clear correspondence between high HRV coupling and the subjective experience of “togetherness" was not found. These results suggest that since autonomic physiological entrainment is observed for non-expert singing, it may be exploited as part of interventions in music therapy or social prescription programs for the general population.

Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

Autonomic Cardiac Reactivity to Painful Procedures Under Hypnosis in Pediatric Emergencies: A Feasibility Study

Pain sensation is characterized by abrupt changes in central nervous system activity producing autonomic reactivity. While clinical hypnosis has demonstrated its benefits for children in pain management, it is not clear whether hypnosis modulated autonomic pain response in children in clinical conditions. Here, we studied autonomic responses under hypnosis to sutures in pediatric emergencies. For that, 42 children (mean age: 6.5 years, range 1.5 to 13) were divided into two groups consecutively (hypnosis and control groups), according to their choice. Time-frequency analysis was applied on RR intervals (heart rate interbeat intervals, or RRI) to estimate parasympathetic reactivity based on high frequency power (HF) and the Analgesia Nociception Index (ANI®) and on sympathetic reactivity (low frequency power [LF]) and LF/HF ratio). We observed that RRI and LF/HF ratio varied according to suture and hypnosis (p < 0.05): RRI was higher and LF/HF ratio was lower during sutures in the hypnosis group in comparison to the control group whereas HF and ANI® increased only during hypnosis. To conclude, hypnosis in pediatric emergencies reduces sympathetic cardiac pain reactivity and could be a marker of pain relief under hypnosis, while parasympathetic activity seems to be a better marker of hypnosis.

Habituation of the electrodermal response - A biological correlate of resilience?

Current approaches to quantifying resilience make extensive use of self-reported data. Problematically, this type of scales is plagued by response distortions–both deliberate and unintentional, particularly in occupational populations. The aim of the current study was to develop an objective index of resilience. The study was conducted in 30 young healthy adults. Following completion of the Connor-Davidson Resilience Scale (CD-RISC) and Depression/Anxiety/Stress Scale (DASS), they were subjected to a series of 15 acoustic startle stimuli (95 dB, 50 ms) presented at random intervals, with respiration, skin conductance and ECG recorded. As expected, resilience (CD-RISC) significantly and negatively correlated with all three DASS subscales–Depression (r = -0.66, p<0.0001), Anxiety (r = -0.50, p<0.005) and Stress (r = -0.48, p<0.005). Acoustic stimuli consistently provoked transient skin conductance (SC) responses, with SC slopes indexing response habituation. This slope significantly and positively correlated with DASS-Depression (r = 0.59, p<0.005), DASS-Anxiety (r = 0.35, p<0.05) and DASS-Total (r = 0.50, p<0.005) scores, and negatively with resilience score (r = -0.47; p = 0.006), indicating that high-resilience individuals are characterized by steeper habituation slopes compared to low-resilience individuals. Our key finding of the connection between habituation of the skin conductance responses to repeated acoustic startle stimulus and resilience-related psychometric constructs suggests that response habituation paradigm has the potential to characterize important attributes of cognitive fitness and well-being–such as depression, anxiety and resilience. With steep negative slopes reflecting faster habituation, lower depression/anxiety and higher resilience, and slower or no habituation characterizing less resilient individuals, this protocol may offer a distortion-free method for objective assessment and monitoring of psychological resilience.

Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low-Stress States: Attention Test

Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should first be able to identify different states based on physiological signals. So, the first aim of this paper is to identify the most appropriate features to detect a subject's high performance state. For that, a database of electrocardiographic (ECG) and photoplethysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals, respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, 26 features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive than the classical sympathetic markers ([Formula: see text] and [Formula: see text]) for identifying this attentional state. State classification accuracy reaches a mean of [Formula: see text], a maximum of [Formula: see text], and a minimum of 85%, in the 100 classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulsewidth, pulse downward slope, and mean pulse rate). These results suggest that attentional states could be identified by PPG.

A Time-Varying Nonparametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

OBJECTIVE

To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in nonstationary conditions.

METHODS

Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the nonstationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging nonstationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed.

RESULTS

In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the nonstationary spectra. The correlation coefficient between theoretical and estimated patterns was even for extremely noisy recordings (signal to noise ratio (SNR) in QTV dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands ( for most pairwise comparisons).

CONCLUSION

The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV.

SIGNIFICANCE

The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death.

A multivariate time-frequency approach for tracking QT variability changes unrelated to heart rate variability

The beat-to-beat variability of the QT interval (QTV) is a marker of ventricular repolarization (VR) dynamics and it has been suggested as an index of sympathetic ventricular outflow and cardiac instability. However, QTV is also affected by RR (or heart rate) variability (RRV), and QTV due to RRV may reduce QTV specificity as a VR marker. Therefore, it would be desirable to separate QTV due to VR dynamics from QTV due to RRV. To do that, previous work has mainly focused on heart rate corrections or time-invariant autoregressive models. This paper describes a novel framework that extends classical multiple inputs/single output theory to the time-frequency (TF) domain to quantify QTV and RRV interactions. Quadratic TF distributions and TF coherence function are utilized to separate QTV into two partial (conditioned) spectra representing QTV related and unrelated to RRV, and to provide an estimates of intrinsic VR dynamics. In a simulation study, a time-varying ARMA model was used to generate signals representing realistic RRV and VR dynamics with controlled instantaneous frequencies and powers. The results demonstrated that the proposed methodology is able to accurately track changes in VR dynamics, with a correlation between theoretical and estimated patterns higher than 0.88. Data from healthy volunteers undergoing a tilt table test were analyzed and representative examples are discussed. Results show that the QTV unrelated to RRV dynamics quickly increased during orthostatic challenge.

Design and evaluation of a ubiquitous chest-worn cardiopulmonary monitoring system for healthcare application: a pilot study

Ambulatory recording of physiological data will provide us deep insight into the physical condition of patients and athletes, and assessing treatment effects and training performances. This study presents a miniature wearable cardiopulmonary monitoring system called "Smart Chest Strap," which consists of an elastic band worn around the user's chest with integrated sensors, a physiological signals acquisition unit, and a mobile phone. The physiological signals including electrocardiogram, respiratory inductance plethysmograph, and accelerations (ACC) are sampled, digitalized, stored, and simultaneously transmitted to a mobile phone via Bluetooth. A medical validation test with participants performing discontinuous incremental treadmill (0-12 km/h) exercise was conducted. The results indicate nearly perfect correlations (0.999, 0.996, 0.994), small mean bias (0.60 BPM, 0.51 BPM, 0.05 g), and narrow limits of agreement (±2.90 BPM, ±1.81 BPM, ±0.09 g) for heart rate (HR), breathing rate (BR), and ACC represented as vector magnitude units (VMUs). There is a general trend of decrease in accuracy, precision, and correlation for HR, BR, and VMU as velocity increases, but these validity statistics are all within acceptable error limits and clinically accepted. The findings demonstrate that the Smart Chest Strap is valid and will have wider applications in healthcare, sports, and scientific research areas.

Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment

Respiratory rate and heart rate variability (HRV) are studied as stress markers in a database of young healthy volunteers subjected to acute emotional stress, induced by a modification of the Trier Social Stress Test. First, instantaneous frequency domain HRV parameters are computed using time-frequency analysis in the classical bands. Then, the respiratory rate is estimated and this information is included in HRV analysis in two ways: 1) redefining the high-frequency (HF) band to be centered at respiratory frequency; 2) excluding from the analysis those instants where respiratory frequency falls within the low-frequency (LF) band. Classical frequency domain HRV indices scarcely show statistical differences during stress. However, when including respiratory frequency information in HRV analysis, the normalized LF power as well as the LF/HF ratio significantly increase during stress ( p-value 0.05 according to the Wilcoxon test), revealing higher sympathetic dominance. The LF power increases during stress, only being significantly different in a stress anticipation stage, while the HF power decreases during stress, only being significantly different during the stress task demanding attention. Our results support that joint analysis of respiration and HRV obtains a more reliable characterization of autonomic nervous response to stress. In addition, the respiratory rate is observed to be higher and less stable during stress than during relax ( p-value 0.05 according to the Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 % ).

Cardiorespiratory Information Dynamics during Mental Arithmetic and Sustained Attention

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