Comparison of heart rate variability spectra using generic relationships of their input signals

Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Various non-parametric methods for HRV estimation have been developed. The spectrum of counts, the instantaneous heart rate spectrum and the interval spectrum are mostly practised. Although extensive literature on their respective properties is available, there seems to be a need for a more complete comparison, eventually resulting in recommendations for applicability. The methods for HRV spectral analysis use their specific transforms of the primary R-R interval series into input signals for spectral computation. This is, in fact, the reason for obtaining different spectra. A basis for comparison is established, revealing the generic relationships of these HRV input signals. It allows for a more systematic evaluation and for further development of the considered methods. The results with simulated and real HRV data show better performance by the spectrum of counts and by a proposed instantaneous heart rate spectrum, obtained using a cubic spline interpolated input signal. The modulation depth of the primary signal can influence the accuracy of the HRV analysis methods.     

Low‐frequency heart rate variability is related to the breath‐to‐breath variability in the respiratory pattern

Changes in heart rate variability (HRV) at “respiratory” frequencies (0.15–0.5 Hz) may result from changes in respiration rather than autonomic control. We now investigate if the differences in HRV power in the low‐frequency (LF) band (0.05–0.15 Hz, HRV_LF) can also be predicted by respiration variability, quantified by the fraction of tidal volume power in the LF (V_LF,n). Three experimental protocols were considered: paced breathing, mental effort tasks, and a repeated attentional task. Significant intra‐ and interindividual correlations were found between changes in HRV_LF and V_LF,n despite all subjects having a respiratory frequency above the LF band. Respiratory parameters (respiratory period, tidal volume, and V_LF,n) could predict up to 79% of HRV_LF differences in some cases. This suggests that respiratory variability is another mechanism of HRV_LF generation, which should be always monitored, assessed, and considered in the interpretation of HRV changes.     

Time and frequency domain methods for heart rate variability analysis: A methodological comparison

The purpose of this paper is to analytically evaluate and compare two of the most common methods for measuring respiration-related heart rate fluctuations: linear detrended heart rate power spectral analysis and the Porges technique of filtered variance. Low-frequency power was removed from instantaneous 4-Hz R-R interval signals using either a first-order linear (linear/spectral technique) or a third-order polynomial (MPF-var technique). The signals were band-pass filtered and analyzed in both the time and frequency domains. Although in most cases the two techniques yielded substantially similar results, the MPF-var technique resulted in signal amplification at a few specific frequencies. The frequency range and effect of amplification of the MPF-var technique were dependent upon the polynomial size, sampling frequency, and frequency content of the signal.     

The effect of ventilation on spectral analysis of heart rate and blood pressure variability during exercise

Heart rate variability (HRV) and systolic blood pressure variability (BPV) during incremental exercise at 50, 75, and 100% of previously determined ventilatory threshold (VT) were compared to that of resting controlled breathing (CB) in 12 healthy subjects. CB was matched with exercise-associated respiratory rate, tidal volume, and end-tidal CO(2) for all stages of exercise. Power in the low frequency (LF, 0.04-0.15 Hz) and high frequency (HF, >0.15-0.4 Hz) for HRV and BPV were calculated, using time-frequency domain analysis, from beat-to-beat ECG and non-invasive radial artery blood pressure, respectively. During CB absolute and normalized power in the LF and HF of HRV and BPV were not significantly changed from baseline to maximal breathing. Conversely, during exercise HRV, LF and HF power significantly decreased from baseline to 100% VT while BPV, LF and HF power significantly increased for the same period. These findings suggest that the increases in ventilation associated with incremental exercise do not significantly affect spectral analysis of cardiovascular autonomic modulation in healthy subjects.     

Evaluation of frequency and time-frequency spectral analysis of heart rate variability as a diagnostic marker of the sleep apnoea syndrome

The sleep apnoea/hypopnoea syndrome (SAHS) elicits a unique heart rate rhythm that may provide the basis for an effective screening tool. The study uses the receiver operator characteristic (ROC) to assess the diagnostic potential of spectral analysis of heart rate variability (HRV) using two methods, the discrete Fourier transform (DFT) and the discrete harmonic wavelet transform (DHWT). These two methods are compared over different sleep stages and spectral frequency bands. The HRV results are subsequently compared with those of the current screening method of oximetry. For both the DFT and the DHWT, the most diagnostically accurate frequency range for HRV spectral power calculations is found to be 0.019-0.036 Hz (denoted by AB2). Using AB2, 15 min sections of non-REM sleep data in 40 subjects produce ROC areas, for the DFT, DHWT and oximetry, of 0.94, 0.97 and 0.67, respectively. In REM sleep, ROC areas are 0.78, 0.79 and 0.71, respectively. In non-REM sleep, spectral analysis of HRV appears to be a significantly better indicator of the SAHS than the current screening method of oximetry, and, in REM sleep, it is comparable with oximetry. The advantage of the DHWT over the DFT is that it produces a greater time resolution and is computationally more efficient. The DHWT does not require the precondition of stationarity or interpolation of raw HRV data.     

Evaluation of frequency and time-frequency spectral analysis of heart rate variability as a diagnostic marker of the sleep apnoea syndrome

The sleep apnoea/hypopnoea syndrome (SAHS) elicits a unique heart rate rhythm that may provide the basis for an effective screening tool. The study uses the receiver operator characteristic (ROC) to assess the diagnostic potential of spectral analysis of heart rate variability (HRV) using two methods, the discrete Fourier transform (DFT) and the discrete harmonic wavelet transform (DHWT). These two methods are compared over different sleep stages and spectral frequency bands. The HRV results are subsequently compared with those of the current screening method of oximetry. For both the DFT and the DHWT, the most diagnostically accurate frequency range for HRV spectral power calculations is found to be 0.019–0.036 Hz (denoted by AB₂). Using AB₂, 15 min sections of non-REM sleep data in 40 subjects produce ROC areas, for the DFT, DHWT and oximetry, of 0.94, 0.97 and 0.67, respectively. In REM sleep, ROC areas are 0.78, 0.79 and 0.71, respectively. In non-REM sleep, spectral analysis of HRV appears to be a significantly better indicator of the SAHS than the current screening method of oximetry, and, in REM sleep, it is comparable with oximetry. The advantage of the DHWT over the DFT is that it produces a greater time resolution and is computationally more efficient. The DHWT does not require the precondition of stationarity or interpolation of raw HRV data.     

Continuous and online analysis of heart rate variability

Spectral analysis of heart rate variability provides a probe to assess the function of the sympathetic and parasympathetic nervous systems. Time-frequency analysis of heart rate variability is useful for investigating autonomic nervous function in patients with syncope or non-sustained ventricular tachycardia, or in anaesthesia, etc. In this paper, we developed an algorithm for continuous and online analysis of heart rate variability. The algorithm was simulated and evaluated in MATLAB, and implemented on the digital signal processor. The electrocardiogram signals from MIT/BIH arrhythmia database and one patient with syncope demonstrate the capability of the proposed method in the continuous and online analysis of heart rate variability.     

New technique for time series analysis combining the maximum entropy method and non-linear least squares method: its value in heart rate variability analysis

A new technique for time series analysis, which is a combination of the maximum entropy method (MEM) for spectral analysis and the non-linear least squares method (LSM) for fitting analysis, is described. In this technique, the MEM power spectral density (MEMPSD) is calculated using a very large lag that could diminish the lag dependence of dominant periods estimated by the MEM analysis. The validity of this large lag is confirmed by the LSM, given that the ten dominant MEM periods are known quantities. To validate the MEM plus LSM technique, it is compared with autoregressive (AR) modelling, by analysing heart rate variability under pharmacological interventions (phenylephrine and trinitroglycerine), using 16 young males. The results indicate that the MEMPSD, when compared with the ARPSD, has numerous periods that could reproduce the original time series much more accurately, as revealed by the LSM analysis. However, both the low- and high-frequency powers with MEMPSD and ARPSDs shift in the expected directions in accordance with the pharmacological effects on the cardiovascular system. The implications of these results are discussed from the theoretical and practical standpoints of the MEM plus LSM technique, compared with AR modelling.     

Very high frequency oscillations in the heart rate and blood pressure of heart transplant patients

The authors studied the recently reported very high frequency (VHF) peaks in the heart rate (HR) and blood pressure (BP) power spectra of heart transplant (HT) patients. These VHF peaks appear at frequencies much higher than the respiratory frequency, in addition to the typical low-frequency and high-frequency peaks. Twenty-five recordings obtained from 13 male HT patients (0.5–65 months following surgery) were compared with recordings from 14 normal male subjects. The ECG, continuous BP and respiration were recorded during 45 min of supine rest. Eight recordings from HT patients were excluded owing to arrhythmias. Spectral analysis was performed on all other recordings. VHF peaks were found in the spectra of both BP and HR in nine recordings obtained from six HT patients. In some cases, the power in the VHF peaks was markedly higher than that of the high-frequency peak. No VHF peaks were observed in eight recordings obtained from four HT patients or in recording from any of the normal subjects. No correlation was found between the incidence of VHF peaks and time after transplant. It was proved that the VHF peaks were not artifactual, and their significance within the framework of the theory of communication systems is discussed. The presence of those peaks was attributed to vagal denervation.     

Intraindividual analysis of instantaneous heart rate variability

We examined the use and potential of quantifying instantaneous heart rate variability (HRV) using a joint time-frequency and time-domain methods. These new techniques are promising, because they provide tools to quantify nonstationary, beat-by-beat changes in HRV components, and are therefore flexible with respect to the design of experimental protocols. A smoothed pseudo-Wigner-Ville distribution (SPWVD) and a time-domain index using polynomial filtering produced fairly coherent estimates of band-specific HRV amplitudes, whereas SPWVD yielded additional information on the frequency characteristics of HRV. Instantaneous HRV appeared to have a complex and a frequency-specific relationship to cardiac activity and electrodermal activity, It is concluded that the time-frequency analysis of HRV is a very promising method for mapping transient changes in the frequency and amplitude characteristics of cardiac dynamics.     

Breathing frequency bias in fractal analysis of heart rate variability

Detrended Fluctuation Analysis (DFA) is an algorithm widely used to determine fractal long-range correlations in physiological signals. Its application to heart rate variability (HRV) has proven useful in distinguishing healthy subjects from patients with cardiovascular disease. In this study we examined the effect of respiratory sinus arrhythmia (RSA) on the performance of DFA applied to HRV. Predictions based on a mathematical model were compared with those obtained from a sample of 14 normal subjects at three breathing frequencies: 0.1 Hz, 0.2 Hz and 0.25 Hz. Results revealed that: (1) the periodical properties of RSA produce a change of the correlation exponent in HRV at a scale corresponding to the respiratory period, (2) the short-term DFA exponent is significantly reduced when breathing frequency rises from 0.1 Hz to 0.2 Hz. These findings raise important methodological questions regarding the application of fractal measures to short-term HRV.     

Smartphone-enabled pulse rate variability: An alternative methodology for the collection of heart rate variability in psychophysiological research

Heart rate variability (HRV) is widely used to assess autonomic nervous system (ANS) function. It is traditionally collected from a dedicated laboratory electrocardiograph (ECG). This presents a barrier to collecting the large samples necessary to maintain the statistical power of between-subject psychophysiological comparisons. An alternative to ECG involves an optical pulse sensor or photoplethysmograph run from a smartphone or similar portable device: smartphone pulse rate variability (SPRV). Experiment 1 determined the simultaneous accuracy between ECG and SPRV systems in n = 10 participants at rest. Raw SPRV values showed a consistent positive bias, which was successfully attenuated with correction. Experiment 2 tested an additional n = 10 participants at rest, during attentional load, and during mild stress (exercise). Accuracy was maintained, but slightly attenuated during exercise. The best correction method maintained an accuracy of +/− 2% for low-frequency spectral power, and +/− 5% for high-frequency spectral power over all points. Thus, the SPRV system records a pulse-to-pulse approximation of an ECG-derived heart rate series that is sufficiently accurate to perform time- and frequency-domain analysis of its variability, as well as accurately reflecting change in autonomic output provided by typical psychophysiological stimuli. This represents a novel method by which an accurate approximation of HRV may be collected for large-sample or naturalistic cardiac psychophysiological research.     

Quantification of the dynamic behavior over time of narrow-band components present in heart rate variability by means of the instantaneous amplitude and frequency

The statistical properties of the time- and frequency-domain characteristics of heart rate variability are known to vary over time. A method is presented to compute the time-varying spectral contents of the characteristic narrow-band frequency components of heart rate variability by means of the instantaneous amplitude and frequency at an optimal time resolution. The instantaneous frequency may show oscillatory, but also irregular periods in time. An index of the instantaneous bandwidth is computed to discriminate between oscillatory and irregular periods and to correct the instantaneous amplitude and frequency for irregular periods.     

Relationships between pulse wave velocity and heart rate variability in healthy men with a range of moderate-to-vigorous physical activity levels

Pulse wave velocity (PWV) is associated with heart rate variability (HRV) in 24–39-year-old men. This study of 40–65-year-old men ranging in moderate-to-vigorous physical activity levels investigated whether (a) PWV is related to spectral HRV, (b) using normalised units for HRV influences that relationship, and (c) HRV predicts PWV when other factors, including age and blood pressure, are accounted for. Subjects were healthy men (N = 115), mean (SD) age 50.8 (7.1) years. Carotid-femoral PWV was measured using Complior. HRV was derived from a 5 min ECG for total, high-frequency, and low-frequency power (TP, HF, and LF, respectively), the LF/HF ratio, and normalised units for HF (HFnu) and LF (LFnu). Non-parametric data were natural log-transformed. PWV was 8.5 (1.4) m s⁻¹. TP, HF, LF, LF/HF, HFnu and LFnu were 1908 (2195) m s², 577 (1034) m s², 457 (514) m s², 1.5 (1.3), 46.8 (17.9), and 49.4 (19.4), respectively. PWV was inversely associated with TP (R ² = 0.061, p = 0.008), HF (R ² = 0.095, p = 0.001), LF (R ² = 0.086, p = 0.002) and HFnu (R ² = 0.040, p = 0.031), but was not associated with LF/HF (R ² = 0.020, p = 0.136) or LFnu (R ² = 0.028 p = 0.076). Only age and systolic blood pressure (adjusted R ² = 0.306, p < 0.001) predicted PWV in multivariate analysis. This study has shown that PWV was weakly associated with TP and HF. The use of normalised units only influenced the relationship between PWV and LF. Finally, relationships between PWV and HRV are mediated through age and systolic blood pressure in this population of men ranging in moderate-to-vigorous physical activity level.     

Nonlinear additive autoregressive model-based analysis of short-term heart rate variability

In this contribution we test the hypothesis that nonlinear additive autoregressive model-based data analysis improves the diagnostic ability based on short-term heart rate variability. For this purpose, a nonlinear regression approach, namely, the maximal correlation method is applied to the data of 37 patients with dilated cardiomyopathy as well as of 37 age- and sex-matched healthy subjects. We find that this approach is a powerful tool in discriminating both groups and promising for further model-based analyses.     

Spectral analysis of heart rate variability signal and respiration in diabetic subjects

The paper deals with methods of processing ECG and respiration signals which aim at detecting parameters whose values may be correlated to normal and diabetic subjects with or without cardiovascular autonomic neuropathy (CAN). Beatto-beat R-R duration values of the ECG and discrete series of respiration are obtained from original signals using a recognition algorithm. Power spectrum analysis (autospectra, cross-spectra and coherence via autoregressive modelling) is carried out on segments of about 200 consecutive cardiac cycles. Spectral parameters of the R-R variability signal are obtained as follows: total power, power of low-frequency (LF) and high-frequency (HF) components, power of the signal which is (or is not) coherent with respiration, in absolute or in percentage values. The experimental protocol considers 40 diabetic patients (21 of whom have diabetic neuropathy) and 14 normals in three different conditions: resting, standing and controlled respiration. The developed spectral parameters seem sensitive enough to differentiate between normal and pathological subjects. These parameters may constitute a quantitative means to be edded to the classical diabetic tests for the diagnosis of cardiovascular autonomic neuropathy.     

Linear and nonlinear analysis of heart rate variability in healthy subjects and after acute myocardial infarction in patients

The objectives of this study were to evaluate and compare the use of linear and nonlinear methods for analysis of heart rate variability (HRV) in healthy subjects and in patients after acute myocardial infarction (AMI). Heart rate (HR) was recorded for 15 min in the supine position in 10 patients with AMI taking β-blockers (aged 57 ± 9 years) and in 11 healthy subjects (aged 53 ± 4 years). HRV was analyzed in the time domain (RMSSD and RMSM), the frequency domain using low- and high-frequency bands in normalized units (nu; LFnu and HFnu) and the LF/HF ratio and approximate entropy (ApEn) were determined. There was a correlation (P < 0.05) of RMSSD, RMSM, LFnu, HFnu, and the LF/HF ratio index with the ApEn of the AMI group on the 2nd (r = 0.87, 0.65, 0.72, 0.72, and 0.64) and 7th day (r = 0.88, 0.70, 0.69, 0.69, and 0.87) and of the healthy group (r = 0.63, 0.71, 0.63, 0.63, and 0.74), respectively. The median HRV indexes of the AMI group on the 2nd and 7th day differed from the healthy group (P < 0.05): RMSSD = 10.37, 19.95, 24.81; RMSM = 23.47, 31.96, 43.79; LFnu = 0.79, 0.79, 0.62; HFnu = 0.20, 0.20, 0.37; LF/HF ratio = 3.87, 3.94, 1.65; ApEn = 1.01, 1.24, 1.31, respectively. There was agreement between the methods, suggesting that these have the same power to evaluate autonomic modulation of HR in both AMI patients and healthy subjects. AMI contributed to a reduction in cardiac signal irregularity, higher sympathetic modulation and lower vagal modulation.     

Recording of ECG signals on a portable MiniDisc recorder for time and frequency domain heart rate variability analysis

Analysis of heart rate variability (HRV) is a non-invasive technique useful for investigating autonomic function in both humans and animals. It has been used for research into both behaviour and physiology. Commercial systems for human HRV analysis are expensive and may not have sufficient flexibility for appropriate analysis in animals. Some heart rate monitors have the facility to provide inter-beat interval (IBI), but verification following collection is not possible as only IBIs are recorded, and not the raw electrocardiogram (ECG) signal. Computer-based data acquisition and analysis systems such as Po-Ne-Mah and Biopac offer greater flexibility and control but have limited portability. Many laboratories and veterinary surgeons have access to ECG machines but do not have equipment to record ECG signals for further analysis. The aim of the present study was to determine whether suitable HRV data could be obtained from ECG signals recorded onto a MiniDisc (MD) and subsequently digitised and analysed using a commercial data acquisition and analysis package. ECG signals were obtained from six Thoroughbred horses by telemetry. A split BNC connecter was used to allow simultaneous digitisation of analogue output from the ECG receiver unit by a computerised data acquisition system (Po-Ne-Mah) and MiniDisc player (MZ-N710, Sony). Following recording, data were played back from the MiniDisc into the same input channel of the data acquisition system as previously used to record the direct ECG. All data were digitised at a sampling rate of 500 Hz. IBI data were analysed in both time and frequency domains and comparisons between direct recorded and MiniDisc data were made using Bland-Altman analysis. Despite some changes in ECG morphology due to loss of low frequency content (primarily below 5 Hz) following MiniDisc recording, there was minimal difference in IBI or time or frequency domain analysis between the two recording methods. The MiniDisc offers a cost-effective approach to intermediate recording of ECG signals for subsequent HRV analysis and also provides greater flexibility than use of human Holter systems.     

Detection of asphyxia using heart rate variability

The long-term aims of this study are to find a parameter derived from the ECG that has a high sensitivity and specificity to asphyxia and, once we know or suspect that asphyxia occurred, to estimate how severe it was. We carried out a pilot study in which 24 adult Wistar rats were anaesthetised and subjected to controlled asphyxia for specified durations. We measured the pH, ‘neurological score’ and the ECG, extracting from this heart rate and heart rate variability (HRV). We have developed a technique capable of detecting asphyxia in less than 1 min, based on monitoring the ECG and estimating HRV by measuring the standard deviation of normal RR intervals (the RR interval is the time interval between two consecutive R-points of the QRS complex). In all cases the heart rate decreased and HRV increased, by an average of 46±33ms in relation to the baseline, at the onset of asphyxia. The comparison of the base level of HRV after and before asphyxia shows promise for the estimation of the severity of the episode; however, the limitations of this study should be noted as they include the small size of the cohort and the methods of analysis.     

Baroreflex sensitivity assessment and heart rate variability: relation to maneuver and technique

In the present study, we examined two baroreflex sensitivity (BRS) issues that remain uncertain: the differences among diverse BRS assessment techniques and the association between BRS and vagal outflow. Accordingly, the electrocardiogram and non-invasive arterial pressure were recorded in 27 healthy subjects, during supine with and without controlled breathing, standing, exercise, and recovery conditions. Vagal outflow was estimated by heart rate variability indexes, whereas BRS was computed by alpha-coefficient, transfer function, complex demodulation in low- and high-frequency bands, and by sequence technique. Our results indicated that only supine maneuvers showed significantly greater BRS values over the high frequency than in the low-frequency band. For maneuvers at the same frequency region, supine conditions presented a larger number of significant differences among techniques. The plots between BRS and vagal measures depicted a funnel-shaped relationship with significant log–log correlations (r=0.880–0.958). Very short latencies between systolic pressure and RR interval series in high-frequency band and strong log–log correlations between frequency bands were found. Higher variability among different baroreflex measurements was associated with higher level of vagal outflow. Methodological assumptions for each technique seem affected by non-baroreflex variation sources, and a modified responsiveness of vagal motoneurons due to distinct stimulation levels for each maneuver was suggested. Thus, highest vagal outflows corresponded to greatest BRS values, with maximum respiratory effect for the high-frequency band values. In conclusion, BRS values and differences across the tested techniques were strongly related to the vagal outflow induced by the maneuvers.