Conference Paper

Effects of Age, BMI, Anxiety and Stress on the Parameters of a Stochastic Model for Heart Rate Variability Including Respiratory Information

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... 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). ...
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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.
... 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. ...
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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. ...
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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.
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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.
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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.
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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 %).
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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.