Analysis of heart rate variability using time-varying frequency bands based on respiratory frequency

Comparison of pulse rate variability from post-auricula and heart rate variability during different body states for healthy subjects

Heart rate variability (HRV) extracted from the electrocardiogram (ECG) is an essential indicator for assessing the autonomic nervous system in clinical. Some scholars have studied the feasibility of pulse rate variability (PRV) instead of HRV. However, there is little qualitative research in different body states. In this paper, the photoplethysmography (PPG) of postauricular and finger and the ECG of fifteen subjects were synchronously collected for comparative analysis. The eleven experiments were designed according to the daily living state, including the stationary state, limb movement state, and facial movement state. The substitutability of nine variables was investigated in the time, frequency, and nonlinearity domain by Passing Bablok regression and Bland Altman analysis. The results showed that the PPG of the finger was destroyed in the limb movement state. There were six variables of postauricular PRV, which showed a positive linear relationship and good agreement (p > 0.05, ratio ≤0.2) with HRV in all experiments. Our study suggests that the postauricular PPG could retain the necessary information of the pulse signal under the limb movement state and facial movement state. Therefore, postauricular PPG could be a better substitute for HRV, daily PPG detection, and mobile health than finger PPG.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

OBJECTIVE

Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep.

METHODS

A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly.

SIGNIFICANCE

An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online.

Heart Rate Variability and Exceptional Longevity

Centenarians are the paradigm of human extreme longevity and healthy aging, because they have postponed, if not avoided, mayor age-related diseases. The purpose of this study was to investigate potential differences in resting heart rate variability (HRV) between young adults, octogenarians, and centenarians and assess whether HRV variables are predictors of all-cause mortality in centenarians. To this end, three groups of participants: young adults (N = 20; 20.6 ± 2.3 years), octogenarians (N = 18; 84.1 ± 2.6 years), and centenarians (N = 17; 101.9 ± 1.9 years) were monitored for 15 min at rest (seated, without moving or talking) to measure RR intervals, from which HRV was evaluated. Our results showed a clear decrease with age in the main parasympathetic HRV variables, as well as in the standard deviation (SD) of the RR series [SD of normal-to-normal interval (SDNN)] and in low frequency (LF) heart rate (HR) oscillations, although differences between octogenarians and centenarians did not reach statistical significance. In 14 centenarians followed until death, only SDNN showed significant correlation (ρ = 0.536; p = 0.048) with survival prognosis. Additionally, SDNN <19 ms was associated with early mortality (≤1 year) in centenarians (Hazard Ratio = 5.72). In conclusion, HRV indices reflecting parasympathetic outflow as well as SDNN and LF all present an age-related reduction, which could be representative of a natural exhaustion of allostatic systems related to age. Moreover, low SDNN values (<19 ms) could be associated with early mortality in centenarians. HRV seems to play a role in exceptional longevity, which could be accounted for by centenarians' exposome.

Cardiohemodynamics and gas analysis rearrangements in response to re-breathing in young males of Russia’s Fareast

The objective of this study was to examine the changes occurred in the heart rate variability, cardiovascular system and gas analysis in response to the test with return breathing in young Caucasian and Native men residing in Magadan region and Chukotka Autonomous District. Methods — Total 345 young men were examined; of them 65 were Native people from Chukotka Autonomous District (ChAD) and 35 Natives from Magadan Region (MR) as well as Caucasians (48 from ChAD and 197 from MR, respectively). Results — Studies have shown that in response to the hypoxic-hypercapnic effect, there were changes in the cardiovascular system, heart rate and gas analysis, with a number of differences depending on ethnicity and the region of residence. Region-related features of cardiohemodynamics were manifested by a more pronounced increase in the systolic blood pressure and heart rate in response to the re-breathing test in the two ethnic groups living in ChAD, which was observed against the background of higher values of the difference “baseline-test” in carbon dioxide levels in the exhaled air. Ethnic differences in our studies were seen due to the pronounced increase in response to the test for the activity of the parasympathetic link of the autonomic nervous system (increased TP and HF), at the background of a decrease in VLF observed only among Caucasians, which was associated with the lowest oxygen concentration in the exhaled air at the peak of the test. It was found that the significant differences in gas analysis identified at rest and at the peak of the test as well as rearrangements of the heart rate and cardiovascular system characteristics in response to the re-breathing in the young subjects residing in different regions of northeast Russia and belonging to different ethnic groups can serve as informative criteria reflecting the region caused ethnic characteristics of the organism. Conclusion — It was found, the most visible and informative parameters for the differences in dynamics of the studied systems demonstrated by the subjects of the two ethnic groups in the two observed regions of the Far East in response to a hypoxic-hypercapnic test with return breathing have been the spectral characteristics of the heart rate (TP, HF, VLF) and gas analysis with the calculation of the difference between the baseline and the peak values (difference in the CO2 and O2 concentration of the test-baseline).

Autonomic Dysfunction Increases Cardiovascular Risk in the Presence of Sleep Apnea

The high prevalence of sleep apnea syndrome (SAS) and its direct relationship with an augmented risk of cardiovascular disease (CVD) have raised SAS as a primary public health problem. For this reason, extensive research aiming to understand the interaction between both conditions has been conducted. The advances in non-invasive autonomic nervous system (ANS) monitoring through heart rate variability (HRV) analysis have revealed an increased sympathetic dominance in subjects suffering from SAS when compared with controls. Similarly, HRV analysis of subjects with CVD suggests altered autonomic activity. In this work, we investigated the altered autonomic control in subjects suffering from SAS and CVD simultaneously when compared with SAS patients, as well as the possibility that ANS assessment may be useful for the early stage identification of cardiovascular risk in subjects with SAS. The analysis was performed over 199 subjects from two independent datasets during night-time, and the effects of the physiological response following an apneic episode, sleep stages, and respiration on HRV were taken into account. Results, as measured by HRV, suggest a decreased sympathetic dominance in those subjects suffering from both conditions, as well as in subjects with SAS that will develop CVDs, which was reflected in a significantly reduced sympathovagal balance (p < 0.05). In this way, ANS monitoring could contribute to improve screening and diagnosis, and eventually aid in the phenotyping of patients, as an altered response might have direct implications on cardiovascular health.

Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review

Objective

To present a systematic review of the use of autonomic nervous system monitoring as a prognostic tool in intensive care units by assessing heart rate variability.

Methods

Literature review of studies published until July 2016 listed in PubMed/Medline and conducted in intensive care units, on autonomic nervous system monitoring, via analysis of heart rate variability as a prognostic tool (mortality study). The following English terms were entered in the search field: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality").

Results

There was an increased likelihood of death in patients who had a decrease in heart rate variability as analyzed via heart rate variance, cardiac uncoupling, heart rate volatility, integer heart rate variability, standard deviation of NN intervals, root mean square of successive differences, total power, low frequency, very low frequency, low frequency/high frequency ratio, ratio of short-term to long-term fractal exponents, Shannon entropy, multiscale entropy and approximate entropy.

Conclusion

In patients admitted to intensive care units, regardless of the pathology, heart rate variability varies inversely with clinical severity and prognosis.

Multivariate classification of Brugada syndrome patients based on autonomic response to exercise testing

Ventricular arrhythmias in Brugada syndrome (BS) typically occur at rest and especially during sleep, suggesting that changes in the autonomic modulation may play an important role in arrhythmogenesis. The autonomic response to exercise and subsequent recovery was evaluated on 105 patients diagnosed with BS (twenty-four were symptomatic), by means of a time-frequency heart rate variability (HRV) analysis, so as to propose a novel predictive model capable of distinguishing symptomatic and asymptomatic BS populations. During incremental exercise, symptomatic patients showed higher HF_nu values, probably related to an increased parasympathetic modulation, with respect to asymptomatic subjects. In addition, those extracted HRV features best distinguishing between populations were selected using a two-step feature selection approach, so as to build a linear discriminant analysis (LDA) classifier. The final features subset included one third of the total amount of extracted autonomic markers, mostly acquired during incremental exercise and active recovery, thus evidencing the relevance of these test segments in BS patients classification. The derived predictive model showed an improved performance with respect to previous works in the field (AUC = 0.92 ± 0.01; Se = 0.91 ± 0.06; Sp = 0.90 ± 0.05). Therefore, based on these findings, some of the analyzed HRV markers and the proposed model could be useful for risk stratification in Brugada syndrome.

Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG

Frequency-domain indices of heart rate variability (HRV) have been used as markers of sympathovagal balance. However, they have been shown to be degraded by interindividual or task-dependent variability, and especially variations in breathing frequency. The study introduces a method to analyze respiration-(vagally) mediated HRV, to better assess subtle variations in sympathovagal balance using ECG recordings. The method enhances HRV analysis by focusing the quantification of respiratory sinus arrhythmia (RSA) gain on the respiratory frequency. To this end, instantaneous respiratory frequency was obtained with ECG-derived respiration (EDR) and was used for variable frequency complex demodulation (VFCDM) of R-R intervals to extract RSA. The ability to detect cognitive stress in 27 subjects (athletes and nonathletes) was taken as a quality criterion to compare our method to other HRV analyses: Root mean square of successive differences, Fourier transform, wavelet transform, and scaling exponent. Three computer-based tasks from MATB-II were used to induce cognitive stress. Sympathovagal index (HF_nu) computed with our method better discriminates cognitive tasks from baseline, as indicated by P values and receiver operating characteristic curves. Here, transient decreases in respiratory frequency have shown to bias classical HRV indices, while only EDR-VFCDM consistently exhibits the expected decrease in the HF_nu index with cognitive stress in both groups and all cognitive tasks. We conclude that EDR-VFCDM is robust against atypical respiratory profiles, which seems relevant to assess variations in mental demand. Given the variety of individual respiratory profiles reported especially in highly trained athletes and patients with chronic respiratory conditions, EDR-VFCDM could better perform in a wide range of applications.

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 % ).

Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method

The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments. The performance of the proposed method in delineating the dynamics of momentary HRV measurement was evaluated with four commonly used time courses of HRV measures on both synthetic time series and real ECG recordings from human subjects and dogs. Our results showed that a smaller time increment could capture more dynamical information on transient changes. Considering a too short increment such as 10 s would cause the indented time courses of the four measures, a 1-min time increment (4-min overlapping) was suggested in the analysis of mHRV in the study. ECG recordings from human subjects and dogs were used to further assess the effectiveness of the proposed method. The pilot study demonstrated that the proposed analysis of mHRV could provide more accurate assessment of the dynamical changes in cardiac activity than the conventional measures of HRV (without time overlapping). The proposed method may provide an efficient means in delineating the dynamics of momentary HRV and it would be worthy performing more investigations.

Separation of respiratory influences from the tachogram: a methodological evaluation

The variability of the heart rate (HRV) is widely studied as it contains information about the activity of the autonomic nervous system (ANS). However, HRV is influenced by breathing, independently of ANS activity. It is therefore important to include respiratory information in HRV analyses in order to correctly interpret the results. In this paper, we propose to record respiratory activity and use this information to separate the tachogram in two components: one which is related to breathing and one which contains all heart rate variations that are unrelated to respiration. Several algorithms to achieve this have been suggested in the literature, but no comparison between the methods has been performed yet. In this paper, we conduct two studies to evaluate the methods' performances to accurately decompose the tachogram in two components and to assess the robustness of the algorithms. The results show that orthogonal subspace projection and an ARMAX model yield the best performances over the two comparison studies. In addition, a real-life example of stress classification is presented to demonstrate that this approach to separate respiratory information in HRV studies can reveal changes in the heart rate variations that are otherwise masked by differing respiratory patterns.

Characterization of dynamic interactions between cardiovascular signals by time-frequency coherence

An assessment of the dynamic interactions between cardiovascular signals can provide valuable information to improve the understanding of cardiovascular control. In this study, two methodologies for the characterization of time-frequency (TF) coherence between cardiovascular signals are described. The methodologies are based on the smoothed pseudo-Wigner-Ville distribution (SPWVD) and multitaper spectrogram (MTSP), and include the automatic assessment of the significance level of coherence estimates. The capability to correctly localize TF regions, where signals are locally coupled, is assessed using computer-generated data, and data from healthy volunteers. The SPWVD allows for the localization of these regions with higher accuracy (AC > 96.9% for SNR ≥ 5 dB) than the MTSP (AC > 84.4% for SNR ≥ 5 dB). In 14 healthy subjects, TF coherence analysis was used to describe the changes, which a tilt table test provokes in the cardiovascular control. Orthostatic stress provoked an increase in the coupling between R-R variability (RRV) and systolic arterial pressure variability; it did not provoke any significant changes in the coupling between RRV and respiration. In HF band, it decreased the strength of the coupling between RRV and pulse interval variability estimated from arterial pressure signal.

HRV analysis in local anesthesia using Continuous Wavelet Transform (CWT)

Spectral analysis of Heart Rate Variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study Continuous Wavelet Transform (CWT) has been used to evaluate the effect of local anesthesia on HRV parameters in a group of fourteen patients undergoing axillary brachial plexus block. A new method which takes signal characteristics into account has been presented for the estimation of the variable boundaries associated with the low and the high frequency band of the HRV signal. The variable boundary method might be useful in cases when the power related to respiration component extends beyond the traditionally excepted range of the high frequency band (0.15-0.4 Hz). The statistical analysis (non-parametric Wilcoxon signed rank test) showed that the LF/HF ratio decreased within an hour of the application of the brachial plexus block compared to the values fifteen minutes prior to the application of the block. These changes were observed in thirteen of the fourteen patients included in this study.

The integral pulse frequency modulation model with time-varying threshold: application to heart rate variability analysis during exercise stress testing

In this paper, an approach for heart rate variability analysis during exercise stress testing is proposed based on the integral pulse frequency modulation (IPFM) model, where a time-varying threshold is included to account for the nonstationary mean heart rate. The proposed technique allows the estimation of the autonomic nervous system (ANS) modulating signal using the methods derived for the IPFM model with constant threshold plus a correction, which is shown to be needed to take into account the time-varying mean heart rate. On simulations, this technique allows the estimation of the ANS modulation on the heart from the beat occurrence time series with lower errors than the IPFM model with constant threshold (1.1% ± 1.3% versus 15.0% ± 14.9%). On an exercise stress testing database, the ANS modulation estimated by the proposed technique is closer to physiology than that obtained from the IPFM model with constant threshold, which tends to overestimate the ANS modulation during the recovery and underestimate it during the initial rest.

Multi-parameter analysis of ECG and respiratory flow signals to identify success of patients on weaning trials

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).

Dynamic assessment of spontaneous baroreflex sensitivity by means of time-frequency analysis using either RR or pulse interval variability

In this study we propose a method to continuously assess the changes of spontaneous baroreflex sensitivity (BRS). Systolic arterial pressure and RR intervals are analyzed by time-frequency analysis to estimate their instantaneous powers as well as the time-course of their spectral coherence. The BRS estimated in classical frequency bands is compared to the BRS estimated in dynamic frequency bands centered on respiratory frequency. The possibility of obtaining reliable estimations of the BRS using the pulse interval from the pressure signal as a surrogate of the RR is considered. Results on a tilt table test database suggest that is possible to obtain reliable BRS estimates just from the analysis of the pressure signal, without the need of ECG recordings.

Spectral analysis of the RR series and the respiratory flow signal on patients in weaning process

A considerable number of patients in weaning process have problems to keep spontaneous breathing during the trial and after it. This study proposes to extract characteristic parameters of the RR series and respiratory flow signal according to the patients' condition in weaning test. Three groups of patients have been considered: 93 patients with successful trials (group S), 40 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but that had to be reintubated before 48 hours (group R). The characterization was performed using spectral analysis of the signals, through the power spectral density, cross power spectral density and Coherence method. The parameters were extracted on the three frequency bands (VLF, LF and HF), and the principal statistical differences between groups were obtained in bands of VLF and HF. The results show an accuracy of 76.9% in the classification of the groups S and F.

On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods

In the last decades, one of the main challenges in the study of heart rate variability (HRV) signals has been the quantification of the low-frequency (LF) and high-frequency (HF) components of the HRV spectrum during non-stationary events. At this regard, different time-frequency and time-varying approaches have been proposed with the aim to track the modification of the HRV spectra during ischaemic attacks, provocative stress testing, sleep or daily-life activities. The quantitative evaluation of power (and frequencies) of the LF and HF components has been approached in various ways depending on the selected time-frequency method. This paper is an excursus through the most common time-frequency/time-varying representation of the HRV signal with a special emphasis on the algorithms employed for the reliable quantification of the LF and HF parameters and their tracking.

Time-frequency analysis of cardiac and respiratory parameters for the prediction of ventilator weaning

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.