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Effects of Age, BMI, Anxiety and Stress on the Parameters of a Stochastic Model for Heart Rate Variability Including Respiratory Information
... When analysing non-stationary signals in medicine, the development of quantitative parameters to describe heart rate variability is of great importance. Heart rate variability (HRV) inferred from the analysis of the tachogram -a series of RR intervals between heart contractions -is known as an important index in cardio-vascular system assessment (CVS) [5,[18][19][20][21]. However, the statistical parameters of HRV (RRNN, SDNN, RMSSD), the spectral characteristics of cardio intervals employing the Fourier transform (ULF, VLF, LF, HF), and the histogram methods given in the Standards can be used only in stationary situations. ...
... Figures 10, 11, 12 and 13 represent the results of DCWT analysis of nonstationary heart rhythm using the STS technique. Figure 10 gives the plot of time behavior for d U L F (t) (19). The heart rhythm assimilation coefficient D U L F (B/A) (21) in the μ = U L F spectral range is 1.92. ...
... The heart rhythm assimilation coefficient D U L F (B/A) (21) in the μ = U L F spectral range is 1.92. The coefficient d U L F (t) (19) reaches the maximal value ≈ 3.8 at t ≈ 900 s. This means that the instantaneous value of E U L F (t) at Stage B (test phase) is approximately 3.8 times greater than the average value of E U L F (A) at Stage A (at rest). ...
The analysis of heart rate variability (HRV) is central for cardiac diagnostics, but the essential non-stationarity of heart rate has started to gain attention only recently. The aim of this work is to develop a set of special new techniques for calculating mathematical indicators of HRV spectral properties associated with non-stationarity in frequency. The analysis is done both for the new model of a tachogram taking into account frequency modulation and for the true tachogram record during head up tilt test. Continuous wavelet transformation of the frequency-modulated signal (CWT) has been derived in analytical form. The local frequency of heart rhythm giving the maximum of CWT has been determined. Treated as another non-stationary signal, this frequency has been subjected to CWT following double CWT procedure (DCWT). The special algorithm for eliminating boundary effects at the computing CWT is used. The transient periods for local frequency, the frequencies of local frequency fluctuation against the main trend and the periods of emergence and attenuation of such fluctuations have been defined by estimating the spectral integrals in the ranges {ULF, VLF, LF, HF}. The combined use of several new techniques taking into account the non-stationary character of heart rate can provide reliable diagnostic results.
... Instead, the chi-square (χ 2 ) test was used to assess the difference in sex categories among frailty groups. CCM parameters were compared between frailty groups using multivariable ANOVA models; age, sex, and BMI were considered as adjusting variables since they have been previously associated with motor and cardiac performance and frailty (16,(42)(43)(44). Cohen's effect size (d) was estimated. ...
Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p<0.01, effect size=0.81$\pm$0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of 82% to 89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty.
... Instead, the chi-square (χ 2 ) test was used to assess the difference in sex categories among frailty groups. CCM parameters were compared between frailty groups using multivariable ANOVA models; age, sex, and BMI were considered as adjusting variables since they have been previously associated with motor and cardiac performance and frailty (16,(42)(43)(44). Cohen's effect size (d) was estimated. ...
Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p<0.01, effect size=0.81$\pm$0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of 82% to 89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty.
... Table 1 presents the three distributions and the respective link functions. We then chose the best model based on the Akaike Information Criterion (AIC) (45) and evaluated the results, both for fixed and random effects (46). We performed TABLE 1 | Distribution of the outcome variable and respective link function. ...
The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.
... HR parameters were compared between frailty groups using ANOVA models; age, sex, and BMI were considered as covariates, and Cohen's effect size (d) was estimated. Age, sex, and, BMI were selected as adjusting variables, since they have been previously associated with HR measures and frailty [37,[48][49][50]. ANOVA analyses for comparing HR parameters across frailty groups were repeated with clinical measures with significant association with frailty as covariates. ...
Background
Previous research showed association between frailty and an impaired autonomic nervous system; however, the direct effect of frailty on heart rate (HR) behavior during physical activity is unclear. The purpose of the current study was to determine the association between HR increase and decrease with frailty during a localized upper-extremity function (UEF) task to establish a multimodal frailty test.
Methods
Older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20 s with the right arm. Wearable gyroscopes were used to measure forearm and upper-arm motion, and electrocardiography were recorded using leads on the left chest. Using this setup, HR dynamics were measured, including time to peak HR, recovery time, percentage increase in HR during UEF, and percentage decrease in HR during recovery after UEF.
Results
Fifty-six eligible participants were recruited, including 12 non-frail (age = 76.92 ± 7.32 years), and 40 pre-frail (age = 80.53 ± 8.12 years), and four frail individuals (age = 88.25 ± 4.43 years). Analysis of variance models showed that the percentage increase in HR during UEF and percentage decrease in HR during recovery were both 47% smaller in pre-frail/frail older adults compared to non-frails ( p < 0.01, effect size = 0.70 and 0.62 for increase and decrease percentages). Using logistic models with both UEF kinematics and HR parameters as independent variables, frailty was predicted with a sensitivity of 0.82 and specificity of 0.83.
Conclusion
Current findings showed evidence of strong association between HR dynamics and frailty. It is suggested that combining kinematics and HR data in a multimodal model may provide a promising objective tool for frailty assessment.
... The model parameters can be used both for the evaluation of an optimal time-frequency estimator and as the response in a regression analysis to investigate the predictive power of physiological variables over the model parameters. Preliminary results of the regression analysis were presented in a previous conference paper [22], based on a data-set of 47 TPs. Additionally to extending the data-set, we have now refined the inference method and included the derivation of the MSE optimal model-based kernel for the TF estimates. ...
In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants’ state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences.
Background
Although previous studies showed that frail older adults are more susceptible to develop cardiovascular diseases, the underlying effect of frailty on heart rate dynamics is still unclear. The goal of the current study was to measure heart rate changes due to normal speed and rapid walking among non-frail and pre-frail/frail older adults, and to implement heart rate dynamic measures to identify frailty status.
Methods
Eighty-eight older adults (≥65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. While performing gait tests, heart rate was recorded using a wearable ECG and accelerometer sensors. Groups consisted of 27 non-frail (age=78.70±7.32) and 61 pre-frail/frail individuals (age=81.00±8.14). The parameters of interest included baseline heart rate measures (mean heart rate and heart rate variability), and heart rate dynamics due to walking (percentage change in heart rate and required time to reach the maximum heart rate).
Results
Respectively for normal and rapid walking condition, pre-frail/frail participants had 46% and 44% less increase in heart rate, and 49% and 27% slower occurrence of heart rate peak, when compared to non-frail older adults (p<0.04, effect size=0.71±0.12). Measures of heart rate dynamics showed stronger associations with frailty status compared to baseline resting-state measures (sensitivity=0.75 and specificity=0.65 using heart rate dynamics measures, compared to sensitivity=0.64 and specificity=0.62 using baseline parameters).
Conclusions
These findings suggest that measures of heart rate dynamics in response to daily activities may provide meaningful markers for frailty screening.
The instantaneous frequency (IF) of a non-stationary signal is usually estimated from a time-frequency distribution (TFD). The IF of heart rate variability (HRV) is an important parameter because the power in a frequency band around the IF can be used for the interpretation and analysis of the respiratory rate but also for a more accurate analysis of heart rate (HR) signals. In this study, we compare the performance of five states of the art kernel-based time-frequency distributions (TFDs) in terms of their ability to accurately estimate the IF of HR signals. The selected TFDs include three widely used fixed kernel methods: the modified B distribution, the S-method and the spectrogram; and two adaptive kernel methods: the adaptive optimal kernel TFD and the recently developed adaptive directional TFD. The IF of the respiratory signal, which is usually easier to estimate as the respiratory signal is a mono-component with small amplitude variations with time, is used as a reference to examine the accuracy of the HRV IF estimates. Experimental results indicate that the most reliable estimates are obtained using the adaptive directional TFD in comparison to other commonly used methods such as the adaptive optimal kernel TFD and the modified B distribution.
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 timefrequency analysis in the classical bands. Then, respiratory rate is estimated and this information is included in HRV analysis in two ways: i) redefining the high frequency (HF) band to be centered at respiratory frequency; ii) 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 Wilcoxon test), revealing higher sympathetic dominance. LF power increases during stress, only being significantly different in stress anticipation stage, while 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, respiratory rate is observed to be higher and less stable during stress than during relax (p-value < 0.05 according to Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 %).
Prior findings indicate that individuals scoring high on vital exhaustion show a dysfunctional stress response (DSR), that is, reduced cortisol reactivity and habituation to psychosocial stressors. The main aim of the present study was to examine whether a DSR may be a vulnerability factor in exhaustion disorder (ED). We examined whether a DSR is present during the early stages of ED, and still is present after recovery. Three groups were studied: 1. Former ED patients (n = 14); 2. persons who during the past 6 month had experienced stress at work and had a Shirom–Melamed Burnout Questionnaire (SMBQ) score over 3.75, considered to indicate a pre-stage of ED (n = 17); 3. persons who had not experienced stress at work during the past 6 months and had a SMBQ score below 2.75 (n = 20). The participants were exposed twice to a virtual version of the Trier Social Stress Test (V-TSST), during which salivary cortisol samples were collected. In addition, high frequency heart rate variability (HF-HRV), heart rate (HR), t-wave amplitude (TWA), and α-amylase were assessed to examine stress reactivity and habituation in the autonomic nervous system (ANS).
Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R–R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2)(p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFA alpha 1 and DFA alpha 2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFA alpha 1 was moderately affected by breathing; and DFA alpha 2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFA alpha 1 was
strongly increased, whereas DFA alpha 2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from –0.14 to –0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.
This study examined the role of gender on short-term heart rate variability (HRV) and the correlation between subjective ratings of stress and HRV in healthy adults. Standardized short-term HRV measurement and self-administered stress response inventory (SRI) were obtained in 441 healthy women and 1440 healthy men. Hierarchical multiple regressions suggested that there was no gender by stress interaction in explaining HRV. However, there were significant gender differences in the associations between stress and HRV (the standard deviation of the NN interval (SDNN), high frequency (HF), low frequency (LF)/HF (F(1, 1878) = 7.706, p < .01; F(1, 1878) = 29.132, p < .01; F(1, 1878) = 49.685, p < .01). In men, only HF (r = -.56, p = .031) showed such an association; whereas in women, the SRI total scores were negatively correlated with SDNN (r = -.103, p = .032), total power (TP) (r = -.104, p = .030), and HF (r = -.129, p = .007), and positively correlated with LF/HF (r = .111, p = .020) when adjusted for age, alcohol drinking, smoking, and caffeine intake. There are gender differences in the association between psychological stress response and HRV. Gender also showed a significant impact on short-term HRV measurement. Given that both clinicians and researchers are increasingly relying on HRV assessment, our work suggest that gender based norms are very important.
In the recent years, short-term heart rate variability (HRV) describing complex variations of beat-to-beat interval series that are mainly controlled by the autonomic nervous system (ANS) has been increasingly analyzed to assess the ANS activity in different diseases and under various conditions. In contrast to long-term HRV analysis, short-term investigations (<30 min) provide a test result almost immediately. Thus, short-term HRV analysis is suitable for ambulatory care, patient monitoring and all those applications where the result is urgently needed. In a previous study, we could show significant variations of 5-min HRV indices according to age in almost all domains (linear and nonlinear) in 1906 healthy subjects from the KORA S4 cohort. Based on the same group of subjects, general gender-related influences on HRV indices are to be determined in this study. Short-term 5-min HRV indices from linear time and frequency domain and fromnonlinear methods (compression entropy, detrended fluctuation analysis, traditional and segmented Poincaré plot analysis, irreversibility analysis, symbolic dynamics, correlation and mutual information analysis) were determined from 782 females and 1124 males. First, we examined the gender differences in two age clusters (25-49 years and 50-74 years). Secondly, we investigated the gender-specific development of HRV indices in five age decade categories, namely for ages 25-34, 35-44, 45-54, 55-64 and 65-74 years. In this study, significant modifications of the indices according to gender could be obtained, especially in the frequency domain and correlation analyses. Furthermore, there were significant modifications according to age in nearly all of the domains. The gender differences disappeared within the last two age decades and the age dependencies disappeared in the last decade. To summarize gender and age influences need to be considered when performing HRV studies even if these influences only partly differ.
This paper addresses a major weakness of traditional heart-rate-variability (HRV) analysis for the purpose of moni- toring stress: sensitivity to respiratory influences. To address this issue, a linear system-identification model of the cardiorespiratory system using commercial heart rate monitors and respiratory sensors was constructed. Subtraction of respiratory driven fluc- tuations in heart rate leads to a residual signal where the effects of mental stress become more salient. We experimentally vali- dated the effectiveness of this method on a binary discrimination problem with two conditions: mental stress of subjects performing cognitive tasks and a relaxation condition. In the process, we also propose a normalization method that can be used to compen- sate for ventilation differences between paced and spontaneous breathing. Our results suggest that, by separating respiration influences, the residual HRV has more discrimination power than traditional HRV analysis for the purpose of monitoring mental stress/load.
Burnout is a mental condition defined as a result of continuous and long-term stress exposure, particularly related to psychosocial factors at work. This paper seeks to examine the psychometric properties of the Shirom-Melamed Burnout Questionnaire (SMBQ) for validation of use in a clinical setting.
Data from both a clinical (319) and general population (319) samples of health care and social insurance workers were included in the study. Data were analysed using both classical and modern test theory approaches, including Confirmatory Factor Analysis (CFA) and Rasch analysis.
Of the 638 people recruited into the study 416 (65%) persons were working full or part time. Data from the SMBQ failed a CFA, and initially failed to satisfy Rasch model expectations. After the removal of 4 of the original items measuring tension, and accommodating local dependency in the data, model expectations were met. As such, the total score from the revised scale is a sufficient statistic for ascertaining burnout and an interval scale transformation is available. The scale as a whole was perfectly targeted to the joint sample. A cut point of 4.4 for severe burnout was chosen at the intersection of the distributions of the clinical and general population.
A revised 18 item version of the SMBQ satisfies modern measurement standards. Using its cut point it offers the opportunity to identify potential clinical cases of burnout.
Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval - the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration - the so called respiratory sinus arrhythmia, RSA) and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of HRV. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the "Physician's Pulse Watch" (a watch with a second hand that could be stopped) in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733) was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895) and advent of digital signal processing techniques in the 1960s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations) that are commonly used to measure HRV.
This paper investigates the time-discrete multitapers that give a mean square error optimal Wigner spectrum estimate for a class of locally stationary processes (LSPs). The accuracy in the estimation of the time-variableWigner spectrum of the LSP is evaluated and compared with other frequently used methods. The optimal multitapers are also approximated by Hermite functions, which is computationally more efficient, and the errors introduced by this approximation are studied. Additionally, the number of windows included in a multitaper spectrum estimate is often crucial and an investigation of the error caused by limiting this number is made. Finally, the same optimal set of weights can be stored and utilized for different window lengths. As a result, the optimal multitapers are shown to be well approximated by Hermite functions, and a limited number of windows can be used for a mean square error optimal spectrogram estimate.
The burnout syndrome denotes a constellation of physical fatigue, emotional exhaustion, and cognitive weariness resulting from chronic stress. Although it overlaps considerably with chronic fatigue as defined in internal medicine, its links with physical illness have not been systematically investigated. This exploratory study, conducted among 104 male workers free from cardiovascular disease (CVD), tested the association between burnout and two of its common concomitants--tension and listlessness--and cardiovascular risk factors. After ruling out five possible confounders (age, relative weight, smoking, alcohol use, and sports activity), the authors found that scores on burnout plus tension (tense-burnout) were associated with somatic complaints, cholesterol, glucose, triglycerides, uric acid, and, marginally, with ECG abnormalities. Workers scoring high on tense-burnout also had a significantly higher low density lipoprotein (LDL) level. Conversely, scores on burnout plus listlessness were significantly associated with glucose and negatively with diastolic blood pressure. The findings warrant further study of burnout as a predictor of cardiovascular morbidity and mortality.
Burnout is characterized by emotional exhaustion, physical fatigue, and cognitive weariness, resulting from prolonged exposure to work-related stress. The authors review the accumulated evidence suggesting that burnout and the related concept of vital exhaustion are associated with increased risk of cardiovascular disease and cardiovascular-related events. The authors present evidence supporting several potential mechanisms linking burnout with ill health, including the metabolic syndrome, dysregulation of the hypothalamic-pituitary-adrenal axis along with sympathetic nervous system activation, sleep disturbances, systemic inflammation, impaired immunity functions, blood coagulation and fibrinolysis, and poor health behaviors. The association of burnout and vital exhaustion with these disease mediators suggests that their impact on health may be more extensive than currently indicated.
This paper treats estimation of the Wigner-Ville spectrum (WVS) of Gaussian continuous-time stochastic processes using Cohen's class of time-frequency representations of random signals. We study the minimum mean square error estimation kernel for locally stationary processes in Silverman's sense, and two modifications where we first allow chirp multiplication and then allow nonnegative linear combinations of covariances of the first kind. We also treat the equivalent multitaper estimation formulation and the associated problem of eigenvalue-eigenfunction decomposition of a certain Hermitian function. For a certain family of locally stationary processes which parametrizes the transition from stationarity to nonstationarity, the optimal windows are approximately dilated Hermite functions. We determine the optimal coefficients and the dilation factor for these functions as a function of the process family parameter
Crame´r-Rao bounds on estimation accuracy are established for estimation problems on arbitrary manifolds in which no set of intrinsic coordinates exists. The frequently encountered examples of estimating either an unknown subspace or a covariance matrix are examined in detail. The set of subspaces, called the Grassmann manifold, and the set of covariance (positive-definite Hermitian) matrices have no fixed coordinate system associated with them and do not possess a vector space structure, both of which are required for deriving classical Crame´r-Rao bounds. Intrinsic versions of the Crame´r-Rao bound on manifolds utilizing an arbitrary affine connection with arbitrary geodesics are derived for both biased and unbiased estimators. In the example of covariance matrix estimation, closed-form expressions for both the intrinsic and flat bounds are derived and compared with the root-mean-square error (RMSE) of the sample covariance matrix (SCM) estimator for varying sample support K. The accuracy bound on unbiased covariance matrix estimators is shown to be about (10/log 10)n/K<sup>1</sup>2/ dB, where n is the matrix order. Remarkably, it is shown that from an intrinsic perspective, the SCM is a biased and inefficient estimator and that the bias term reveals the dependency of estimation accuracy on sample support observed in theory and practice. The RMSE of the standard method of estimating subspaces using the singular value decomposition (SVD) is compared with the intrinsic subspace Crame´r-Rao bound derived in closed form by varying both the signal-to-noise ratio (SNR) of the unknown p-dimensional subspace and the sample support. In the simplest case, the Crame´r-Rao bound on subspace estimation accuracy is shown to be about (p(n-p))<sup>1</sup>2-1/2/SNR<sup>-1</sup>2/ rad for p-dimensional subspaces. It is seen that the SVD-based method yields accuracies very close to the Crame´r-Rao bound, establishing that the principal invariant subspace of a random sample provides an excellent estimator of an unknown subspace. The analysis approach developed is directly applicable to many other estimation problems on manifolds encountered in signal processing and elsewhere, such as estimating rotation matrices in computer vision a- nd estimating subspace basis vectors in blind source separation.
The study of a time-frequency image is often the method of choice to address key issues in cognitive electrophysiology. The quality of the time-frequency representation is crucial for the extraction of robust and relevant features, thus leading to the demand for highly performing spectral estimators. We consider a stochastic model, known as Locally Stationary Processes, based on the modulation in time of a stationary covariance function. The flexibility of the model makes it suitable for a wide range of time-varying signals, in particular EEG signals. Previous works provided the theoretical expression of the mean-square error optimal kernel for the computation of the Wigner-Ville spectrum. The introduction of a novel inference method for the model parameters permits the computation of the optimal kernel in real-world data cases. The obtained MSE optimal time-frequency estimator is compared with other commonly used methods in a simulation study, confirming the error reduction. Optimal spectral estimates are presented for the case study, consisting of EEG data collected within a research on memory retrieval.
Background and objective:
Currently Coronary Artery Disease (CAD) is one of the most prevalent diseases, and also can lead to death, disability and economic loss in patients who suffer from cardiovascular disease. Diagnostic procedures of this disease by medical teams are typically invasive, although they do not satisfy the required accuracy.
Methods:
In this study, we have proposed a methodology for the automatic diagnosis of normal and Coronary Artery Disease conditions using Heart Rate Variability (HRV) signal extracted from electrocardiogram (ECG). The features are extracted from HRV signal in time, frequency and nonlinear domains. The Principal Component Analysis (PCA) is applied to reduce the dimension of the extracted features in order to reduce computational complexity and to reveal the hidden information underlaid in the data. Finally, Support Vector Machine (SVM) classifier has been utilized to classify two classes of data using the extracted distinguishing features. In this paper, parameters of the SVM have been optimized in order to improve the accuracy.
Results:
Provided reports in this paper indicate that the detection of CAD class from normal class using the proposed algorithm was performed with accuracy of 99.2%, sensitivity of 98.43%, and specificity of 100%.
Conclusions:
This study has shown that methods which are based on the feature extraction of the biomedical signals are an appropriate approach to predict the health situation of the patients.
Several studies have shown that acute psychosocial stress and chronic psychosocial stress reduce heart rate variability (HRV). It is likely that individuals suffering from burnout have reduced HRV, as a consequence of the long-term stress exposure. This study investigated HRV in 54 patients with clinical burnout (40 women and 14 men) and in 55 individuals reporting low burnout scores (healthy; 24 women and 31 men) and 52 individuals reporting high burnout scores (non-clinical burnout; 33 women and 19 men). The participants underwent a 300 s ECG recording in the supine position. Standard deviation of normal R-R intervals (SDNN) and the root mean square of successive normal interval differences (RMSSD) were derived from time domain HRV analysis. Frequency domain HRV measures; total power (TP), low frequency power (LF), high frequency power (HF), and LF/HF ratio were calculated. All HRV measures, except LF/HF ratio, were lower in the clinical burnout patients compared to both the non-clinical burnout group and the healthy group. The difference was larger between the patients and the healthy group than between the patients and the non-clinical burnout group. HRV did not differ significantly between the non-clinical burnout group and the healthy group. Low HRV in burnout patients may constitute one of the links to associated adverse health, since low HRV reflects low parasympathetic activity – and accordingly low anabolic/regenerative activity.
One of the primary tenets of polyvagal theory dictates that parasympathetic influence on heart rate, often estimated by respiratory sinus arrhythmia (RSA), shifts rapidly in response to changing environmental demands. The current standard analytic approach of aggregating RSA estimates across time to arrive at one value fails to capture this dynamic property within individuals. By utilizing recent methodological developments that enable precise RSA estimates at smaller time intervals, we demonstrate the utility of computing time-varying RSA for assessing psychophysiological linkage (or synchrony) in husband-wife dyads using time-locked data collected in a naturalistic setting.
© 2015 Society for Psychophysiological Research.
The relationship between on the one hand personality traits and, on the other hand, neuropsychological test performance, self-rated distress symptoms, psychosocial work exposure, and social interaction patterns, was studied in a group of healthy women (N=101). All subjects completed the Karolinska Scales of Personality inventory (KSP) and the majority of the subjects (n=88) completed the State Trait Anxiety Inventory-Trait Scale (STAI-T). Few relationships between personality traits and neuropsychological test results were observed. According to expectations age and education influenced neuropsychological test performance. As for the rating inventories, the high-anxiety half of the subjects typically reported more symptoms and lower social interaction scores than the low-anxiety half. The results suggest that reporting in Euro-Quest (EQ), General Health Questionnaire-30 (GHQ-30), Symptom Check List-90 (SCL-90), Interview Schedule for Social Interaction (ISSI) and the Job Content Questionnaire are sensitive to differences in trait anxiety levels as defined by STAI-T or the psychic anxiety scale in the KSP inventory. The broad impact of trait anxiety on symptom reporting indicates the importance of checking for and possibly controlling for this dimension. Furthermore, the ISSI and JCQ inventories should not be thought of as assessing social circumstances independently from personality traits such as social desirability.
Addresses the chronic occupational health problem of burnout, beginning with an examination of the Maslach Burnout Inventory, the Pines' Burnout Measure, and the Shirom-Melamed Burnout Measure. The author conducts a critical review of the various approaches to burnout. The central part of the chapter is a review of the research literature, with an emphasis placed on longitudinal studies as well as the antecedents, symptoms, and consequences of burnout. In addition, the chapter addresses personality traits associated with burnout, burnout and job performance, burnout and health, burnout at the organizational level of analysis, and approaches to reduce burnout in work organizations. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
discusses the conceptual meaning of burnout, in an attempt to identify its unique content and theoretical underpinnings / definitional approaches
construct validity of burnout / concurrent and discriminant validity / convergent validity
consider a few epidemiological aspects of burnout
burnout among teachers is taken as a case in point, offering tentative generalizations which may apply to other people occupations
covers the issues of the causes and consequences of burnout
outline the major findings of the few longitudinal studies which have been undertaken in this field of study
details recommended directions for future research, to enhance our understanding of burnout (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The purpose of this study was to investigate the immune, endocrine, and metabolic correlates of burnout among women.
Forty-three participants with high and 20 participants with low scores for the Shirom-Melamed Burnout Questionnaire were compared in terms of subjective symptoms, job strain, social support, plasma levels of prolactin, tumor necrosis factor alpha (TNF-alpha), transforming growth factor beta (TGF-beta), C-reactive protein (CRP), neopterin, serum levels of dehydroepiandrosterone sulphate (DHEAs), progesterone, estradiol, cortisol, and glycated hemoglobin (HbA1C) in whole blood.
Besides reporting more job strain, less social support at work, and higher levels of anxiety, depression, vital exhaustion (VE), and sleep impairments, participants with high burnout manifested higher levels of TNF-alpha and HbA1C, independent of confounders including depression.
Among women, burnout seems to involve enhanced inflammatory responses and oxidative stress.
The relationship between test results from the Meta-Contrast Technique (MCT) and self-reports from Karolinska Scales of Personality (KSP), or the State Trait Anxiety Inventory (STAI-T), was investigated in 100 healthy women. Additionally, it was investigated whether age and cognitive abilities influenced the reports of picture recognition thresholds in the MCT. The results showed no agreement between the different ways to assess anxiety and defensiveness. However, age consistently predicted later reports of the perceptual recognition thresholds A1 (the car), A2 (the face) and C-phase. The WAIS-R Digit Symbol Score predicted earlier reporting of the recognition thresholds A1 and A2, but did not predict the final criteria for correct recognition (C-phase). The KSP aggression factor only predicted an earlier report of recognition threshold A2. The absence of a simple relationship between the different ways to assess anxiety and defensiveness, and the observed relationships regarding perceptual threshold levels, corroborates previous findings.
A new kind of random process, the locally stationary random process, is defined, which includes the stationary random process as a special case. Numerous examples of locally stationary random processes are exhibited. By the generalized spectral density Psi(omega, omega prime) of a random process is meant the two-dimensional Fourier transform of the covariance of the process; as is well known, in the case of stationary processes, Psi(omega, omega prime) reduces to a positive mass distribution on the line omega = omega prime in the omega, omega prime plane, a fact which is the gist of the familiar Wiener-Khintchine relations. In the case of locally stationary random processes, a relation is found between the covariance and the spectral density which constitutes a natural generalization of the Wiener-Khintchine relations.
RStudio: Integrated Development Environment for R
RStudio Team (2015). RStudio: Integrated Development
Environment for R. RStudio, Inc., Boston, MA.
Original article: Bullying at work, health outcomes, and physiological stress response
- Å M Hansen
- A Hogh
- R Persson
- B Karlson
- A H Garde
- P Ørbaek
Hansen,Å. M., Hogh, A., Persson, R., Karlson, B., Garde,
A. H., and Ørbaek, P. (2006). Original article: Bullying at work, health outcomes, and physiological stress
response. Journal of Psychosomatic Research, 60:63
-72.