Effect of different breathing patterns on nonlinearity of heart rate variability

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.

Identification of a High-Risk Group of New-Onset Cardiovascular Disease in Occupational Drivers by Analyzing Heart Rate Variability

Purpose: This cohort study evaluated the effectiveness of noninvasive heart rate variability (HRV) analysis to assess the risk of cardiovascular disease over a period of 8 years. Methods: Personal and working characteristics were collected before biochemistry examinations and 5 min HRV tests from the Taiwan Bus Driver Cohort Study (TBDCS) in 2005. This study eventually identified 161 drivers with cardiovascular disease (CVD) and 627 without between 2005 and 2012. Estimation of the hazard ratio was analyzed by using the Cox proportional-hazards model. Results: Subjects with CVD had an overall lower standard deviation of NN intervals (SDNN) than their counterparts did. The SDNN index had a strong association with CVD, even after adjusting for risk factors. Using a median split for SDNN, the hazard ratio of CVD was 1.83 (95% CI = 1.10–3.04) in Model 1 and 1.87 (95% CI = 1.11–3.13) in Model 2. Furthermore, the low-frequency (LF) index was associated with a risk of CVD in the continuous approach. For hypertensive disease, the SDNN index was associated with increased risks in both the continuous and dichotomized approaches. When the root-mean-square of the successive differences (RMSSDs), high frequency (HF), and LF were continuous variables, significant associations with hypertensive disease were observed. Conclusions: This cohort study suggests that SDNN and LF levels are useful for predicting 8 year CVD risk, especially for hypertensive disease. Further research is required to determine preventive measures for modifying HRV dysfunction, as well as to investigate whether these interventions could decrease CVD risk among professional drivers.

Exhaled breath compositions under varying respiratory rhythms reflects ventilatory variations: translating breathomics towards respiratory medicine

Control of breathing is automatic and its regulation is keen to autonomic functions. Therefore, involuntary and voluntary nervous regulation of breathing affects ventilatory variations, which has profound potential to address expanding challenges in contemporary pulmonology. Nonetheless, the fundamental attributes of the aforementioned phenomena are rarely understood and/or investigated. Implementation of unconventional approach like breathomics may leads to a better comprehension of those complexities in respiratory medicine. We applied breath-resolved spirometry and capnometry, non-invasive hemodynamic monitoring along with continuous trace analysis of exhaled VOCs (volatile organic compounds) by means of real-time mass-spectrometry in 25 young and healthy adult humans to investigate any possible mirroring of instant ventilatory variations by exhaled breath composition, under varying respiratory rhythms. Hemodynamics remained unaffected. Immediate changes in measured breath compositions and corresponding variations occurred when respiratory rhythms were switched between spontaneous (involuntary/unsynchronised) and/or paced (voluntary/synchronised) breathing. Such changes in most abundant, endogenous and bloodborne VOCs were closely related to the minute ventilation and end-tidal CO₂ exhalation. Unprecedentedly, while preceded by a paced rhythm, spontaneous rhythms in both independent setups became reproducible with significantly (P-value ≤ 0.005) low intra- and inter-individual variation in measured parameters. We modelled breath-resolved ventilatory variations via alveolar isoprene exhalation, which were independently validated with unequivocal precision. Reproducibility i.e. attained via our method would be reliable for human breath sampling, concerning biomarker research. Thus, we may realize the actual metabolic and pathophysiological expressions beyond the everlasting in vivo physiological noise. Consequently, less pronounced changes are often misinterpreted as disease biomarker in cross-sectional studies. We have also provided novel information beyond conventional spirometry and capnometry. Upon clinical translations, our findings will have immense impact on pulmonology and breathomics as they have revealed a reproducible pattern of ventilatory variations and respiratory homeostasis in endogenous VOC exhalations.

A morphological approach to detect respiratory phases of seismocardiogram

This paper presents a new approach to identify the respiratory phases of heart cycles from acceleration signals (i.e., seismocardiogram) recorded from the sternum, in back to front direction. The acceleration signals were recorded simultaneously with a single lead electrocardiogram (ECG), and the respiratory signal (using a chest band strain gauge) from 20 healthy subjects. Two accelerometer-derived respiration (ADR) signals were computed by computing the lower and upper envelope of the accelerometer signal. In the proposed methodology, for each subject a metric so-called, the piecewise total harmonic distortion (THD) was used to identify which one of lower and upper envelopes is the best ADR for detecting respiratory phases. The accuracy of piecewise THD in the selection of the correct envelope of SCG signal as an estimation of ADR is 84.6%. Consequently, respiratory phases of heart cycles were identified using the estimated ADR signals. Results confirm that the proposed envelope detection based ADR technique can detect respiratory phases of heartbeats with the accuracy of above 75%. In other words, using aforementioned methods, THD thresholding and piecewise THD, the capability of ADR signal to detect respiratory phases is increased approximately 14% compared to the lower envelope of the accelerometer (ADRLower) and 4% compared to the upper envelope of accelerometer signal (ADRUpper).

Effects of guided breath exercise on complex behaviour of heart rate dynamics

INTRODUCTION

Cardiac autonomic regulation is influenced by changes in respiratory rate, which has been demonstrated by linear analysis of heart rate variability (HRV). Conversely, the complex behaviour is not well defined for HRV during this physiological state. In this sense, Higuchi Fractal Dimension is applied directly to the time series. It analyses the fractal dimension of discrete time sequences and is simpler and faster than correlation dimension and many other classical measures derived from chaos theory. We investigated chaotic behaviour of heart rate dynamics during guided breath exercises.

METHOD

We investigated 21 healthy male volunteers aged between 18 and 30 years. HRV was analysed 10 min before and 10 min during guided breath exercises. HRV was analysed in the time and frequency domain for linear analysis and through HFD for non-linear analysis.

RESULTS

Linear analysis indicated that SDNN, pNN50, RMSSD, LF, HF and LF/HF increased during guided breath exercises. HFD analysis illustrated that between K_max 20 to K_max 120 intervals, was enhanced during guided breath exercises.

CONCLUSION

Guided breath exercises acutely increased chaotic behaviour of HRV measured by HFD.