Respiration response curve analysis of heart rate variability

A noninvasive study was conducted on intact awake humans to characterize the dynamic response of the heart to the vagus during slow-, comfortable-, and fast-paced respiration (8, 12, and 18 breaths/min), under both sitting and standing conditions. The respiration response curve (RRC) of respiration-associated vagal effects on the heart was estimated, and characteristics of entrainment and frequency dependence on respiration were demonstrated. It was shown that the degree of entrainment and magnitude of phase resetting decrease with increase of pacing rate from 8 to 18 breaths/min. Further, the RRC was examined by overlapping equivalent phase shifts in different respiration cycles. This examination of the RRC can help one not only to find the common pattern underlying the RRC during different respiration cycles but also to perceive its variation related to degree of entrainment     

Assessment of autonomic regulation of heart rate variability by the method of complex demodulation

Complex demodulation was used to examine the effect of both divisions of the autonomic nervous system (sympathetic and parasympathetic) on heart rate. Data were analyzed from dogs during classical conditioning procedures which caused different changes in the autonomic regulation of heart rate. Two significant peaks in the heart rate variability spectrum were examined by this technique. The amplitude of the peak at the respiration frequency showed parasympathetic changes, while the amplitude of the low frequency peak (0-0.124 Hz) showed both sympathetic and parasympathetic effects. Complex demodulation results at these frequencies clearly showed the activities of both branches of the autonomic nervous system in regulating heart rate. During the CS+ period, when trained dogs were presented with a tone predicting a subsequent shock, the observed tachycardia was due to decreased parasympathetic activity and a transient increase in sympathetic activity. During the CS- period where a different tone predicts no shock, parasympathetic and sympathetic activities were unchanged from the baseline condition. The use of complex demodulation enables us to examine autonomic contributions to heart rate regulation in conditioning and a variety of other physiological and environmental conditions where autonomic input can be expected to change rapidly.     

A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia

It has been reported that the sympathovagal balance (SB) can be quantified by heart rate (HR) via the low-frequency (LF) to high-frequency (HF) spectral power ratio LF/HF. In this paper, an investigation of the relationship between the autonomic nervous system (ANS) and non-sustained ventricular tachycardia (NSVT) is presented. A wavelet transform (WT)-based approach for short-time heart rate variability (HRV) assessments is proposed for this aspect of analysis. The study was conducted on an RR-interval database consisting of 87 NSVT, 61 ischemic and five normal episodes. First, instantaneous SB estimates were generated by the proposed method. Then, waveforms of the WT-based SB evolutions were quantitatively examined. Numerical results showed that while a majority of SB waveforms (about 71%) derived from the non-NSVT population (i.e., ischemic and normal) appeared to come near oscillating with certain fixed levels, approximate 75% of SB evolutions underwent significantly rapid increases prior to the onset of NSVT, suggesting that an abrupt sympathovagal imbalance might partly account for the occurrence of NSVT.     

Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes

This paper introduces a modified principal dynamic modes (PDM) method, which is able to separate the dynamics of sympathetic and parasympathetic nervous activities. The PDM is based on the principle that among all possible choices of expansion bases, there are some that require the minimum number of basis functions to achieve a given mean-square approximation of the system output. Such a minimum set of basis functions is termed PDMs of the nonlinear system. We found that the first two dominant PDMs have similar frequency characteristics for parasympathetic and sympathetic activities, as reported in the literature. These results are consistent for all nine of our healthy human subjects using our modified PDM approach. Validation of the purported separation of parasympathetic and sympathetic activities was performed by the application of the autonomic nervous system blocking drugs atropine and propranolol. With separate applications of the respective drugs, we found a significant decrease in the amplitude of the waveforms that correspond to each nervous activity. Furthermore, we observed near complete elimination of these dynamics when both drugs were given to the subjects. Comparison of our method to the conventional low-frequency/high-frequency ratio shows that our proposed approach provides more accurate assessment of the autonomic nervous balance. Our nonlinear PDM approach allows a clear separation of the two autonomic nervous activities, the lack of which has been the main reason why heart rate variability analysis has not had wide clinical acceptance.     

Constituent factors of heart rate variability ALLSTAR big data analysis

Various indices have been reported regarding heart rate variability (HRV), but many of them correlate to each other, suggesting the existence of the underlying common factors. We tried to extract factors underlying HRV indices and investigated their features. Using big data of 24-h electrocardiogram (ECG) called Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR), we calculated 4 time-domain, 4 frequency-domain, and 2 nonlinear HRV indices and the amplitude of cyclic variation of heart rate (Acv) in 113,793 men and 140,601 women with sinus rhythm ECG. Factor analysis revealed that there were two factors with eigenvalue?≥?1 by which 91% of variance among the HRV indices was explained. Factor 1 that was strongly contributed by very-low frequency, low frequency (LF), and high frequency (HF) components and Acv and it increased with age from 0 to 20 year, then decreased until 65 year, and increased slightly after 80 year. It also increased with daily physical activity at the mild level of activity. Factor 2 that was contributed strongly by scaling exponent α₁ and LF-to-HF ratio increased with age until 35 year, plateaued between 35 and 55 year, and decreased thereafter. It also increased with mild to moderate physical activity. HRV indices are constituted by two common factors relating to cardiac vagal function and complexity of heart rate dynamics, respectively, which differ in the relationships with age and physical activity from each other. Although many indices have been proposed for HRV, their constituent factors may be a few.

     

A novel robust index to assess beat-to-beat variability in heart rate time-series analysis

A new index is proposed to estimate the variance of the differentiated heart rate (RR) time series from its truncated histogram. The index is more robust to artifacts than the standard deviation of the differentiated RR time series (rMSDD) and, unlike the pNN50, does not saturate for very high or very low heart rate variability.     

Local holder exponent analysis of heart rate variability in preterm infants

Heart rate variability (HRV) displays scale-invariant fractal properties. Recent studies have revealed multifractal properties in the healthy human HRV, which could be characterized by singularities with various strength of local H?lder exponents embedded in HRV. In this paper, HRV time series from preterm infants, whose autonomic nervous system undergoes dramatic development, were collected longitudinally. Changes in fractality/multifractality of those HRV time series as the postmenstrual age were examined in order to see if they could quantify development of the autonomic nervous system. Temporal structure of the singularities at several representative time scales was also analyzed to show that intersingular event intervals could be well described by "power law distribution," and the singular events appeared with age-dependent long-range correlation in its strength. Detailed analyses suggested that fractality and multifractality of HRV, respectively, could quantify the development of the respiratory center and the parasympathetic nervous system in the preterm infants. The results obtained in this study might be beneficial for detecting occurrences of life threatening singular events such as big apnea in preterm infants.     

A new and fast nonlinear method for association analysis of biosignals

In this paper, we present some original theoretical aspects of a fast nonlinear association measure based on the work of Cramér. The features of this new measure--the V measure--when applied to biosignals are also shown using simulated time series. A comparative study with other well-known association measures available in the literature of biosignals is presented. V was found to be twice as fast and more robust to nonlinearities than the classical cross-correlation ratio (r2) and more than 100 times faster than the nonlinear regression coefficient (h2), presenting similar behavior in the presence of nonlinear simulated situations. This new measure is very fast and versatile. It is appropriate to deal with nonlinear relations presenting usually a sharp peak in the association function enabling a high degree of selectivity for maxima detection. It seems to constitute an improvement over linear methods of association which is faster and more robust to the existing nonlinearities. It can be used as an alternative to more complex nonlinear association measures when computational speed is an important feature.     

A nonlinear time-frequency analysis method

An alternative method of time-frequency signal analysis is introduced and is compared with the conventional discrete Fourier transfer (DFT)-based methods. The structure of the proposed algorithm and its mathematical properties are presented. The proposed algorithm has a nonlinear structure that provides a frequency-adaptivity feature. It can be implemented both in analog and in digital form and is particularly suitable for real-time applications where computational efficiency is important. A comparison is made with the DFT in terms of algorithm efficiency, sensitivity, and complexity. It is shown that compared with the DFT, the algorithm is more efficient for real-time applications and is less sensitive to noise and variations in the frequency and sampling rate while maintaining simplicity of the structure and computational efficiency. Slower convergence rate of the algorithm, compared with the DFT, constitutes its main shortcoming.     

Distortion properties of the interval spectrum of IPFM generatedheartbeats for heart rate variability analysis

The integral pulse frequency modulation (IPFM) model converts a continuous-time signal into a modulated series of event times, often represented as a pulse train. The IPFM process is important to the field of heart rate variability (HRV) as a simple model of the sinus modulation of heart rate. In this paper, we discuss the distortion properties associated with employing the interval spectrum for the recovery of the input signal from an IPFM process's output pulse train. The results state, in particular for HRV, how precisely the interval spectrum can be used to infer the modulation signal responsible for a series of heartbeats. We have developed a detailed analytical approximation of the interval spectrum of an IPFM process with multiple sinusoids as the input signal. Employing this result, we describe the structure and the distortion of the interval spectrum. The distortion properties of the interval spectrum are investigated systematically for a pair of frequency components. The effects of linear and nonlinear distortion of the fundamentals, the overall contribution of harmonic components to the total power, the relative contribution of "folded back" power due to aliasing and the total distortion of the input spectrum are investigated. We also provide detailed comparisons between the interval spectrum and the spectrum of counts (SOC). The spectral distortion is significant enough that caution should be taken when interpreting the interval spectrum, especially for high frequencies or large modulation amplitudes. Nevertheless, the distortion levels are not significantly larger than those of the SOC. Therefore, the spectrum of intervals may be considered a viable technique that suffers more distortion than the SOC.     

Real-time heart rate variability extraction using the Kaiser window

A new method for real-time heart rate variability (HRV) detection from the R-wave signal, based on the integral pulse frequency modulation (IPFM) model and its similarity to pulse position modulation, is presented. The proposed method exerts lowpass filtering with a Kaiser window. It can also be used for off-line HRV analysis in both the time and frequency domains. Real-time bandpass filtering as a new HRV investigation method and as a by-product of the proposed algorithm is also introduced. Furthermore, the discrete time domain version of the French-Holden algorithm is developed, and it is thoroughly proved that lowpass filtering is an ideal method for detection of HRV     

Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.     

Individual time-dependent spectral boundaries for improved accuracy in time-frequency analysis of heart rate variability

Heart rate variability (HRV) is a major noninvasive technique for evaluating the autonomic nervous system (ANS). Use of time-frequency approach to analyze HRV allows investigating the ANS behavior from the power integrals, as a function of time, in both steady-state and non steady-state. Power integrals are examined mainly in the low-frequency and the high-frequency bands. Traditionally, constant boundaries are chosen to determine the frequency bands of interest. However, these ranges are individual, and can be strongly affected by physiologic conditions (body position, breathing frequency). In order to determine the dynamic boundaries of the frequency bands more accurately, especially during autonomic challenges, we developed an algorithm for the detection of individual time-dependent spectral boundaries (ITSB). The ITSB was tested on recordings from a series of standard autonomic maneuvers with rest periods between them, and the response to stand was compared to the known physiological response. A major advantage of the ITSB is the ability to reliably define the mid-frequency range, which provides the potential to investigate the physiologic importance of this range.     

Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model

The heart rate variability (HRV) is an extended tool to analyze the mechanisms controlling the cardiovascular system. In this paper, the integral pulse frequency modulation model (IPFM) is assumed. It generates the beat occurrence times from a modulating signal. This signal is thought to represent the autonomic nervous system action, mostly studied in its frequency components. Different spectral estimation methods try to infer the modulating signal characteristics from the available beat timing on the electrocardiogram signal. These methods estimate the spectrum through the heart period (HP) or the heart rate (HR) signal. We introduce a new time domain HRV signal, the Heart Timing (HT) signal. We demonstrate that this HT signal, in contrast with the HR or HP, makes it possible to recover an unbiased estimation of the modulating signal spectra. In this estimation we avoid the spurious components and the low-pass filtering effect generated when analyzing HR or HP.     

A new method of spectral analysis and sample rate reduction for band-limited signals

A new method for spectrum analysis and sample rate reduction based upon the classical sampling theorem is presented. It is shown that bandshifting sinusoids are not required for frequency translation. This results in efficient implementations for bandpass spectral analysis.     

Threshold modeling of autonomic control of heart rate variability

Even in the absence of external perturbation to the human cardiovascular system, measures of cardiac function, such as heart rate, vary with time in normal physiology. The primary source of the variation is constant regulation by a complex control system which modulates cardiac function through the autonomic nervous system. Here, we present methods of characterizing the statistical properties of the underlying processes that result in variations in ECG R-wave event times within the framework of an integrate-and-fire model. We first present techniques for characterizing the noise processes that result in heart rate variability even in the absence of autonomic input. A relationship is derived that relates the spectrum of R-R intervals to the spectrum of the underlying noise process. We then develop a technique for the characterization of the dynamic nature of autonomically related variability resulting from exogenous inputs, such as respiratory-related modulation. A method is presented for the estimation of the transfer function that relates the respiratory-related input to the variations in R-wave event times. The result is a very direct analysis of autonomic control of heart rate variability through noninvasive measures, which provides a method for assessing autonomic function in normal and pathological states.     

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.     

A new design project of the line feed structure for large spherical radio telescope and its nonlinear dynamic analysis

A new design of the line feed structure for a large spherical radio telescope (LSRT) is presented in this paper. Integrated mechanical, electronic, optic and automatic control technologies are employed to make considerable improvement upon the Arecibo spherical radio telescope in Puerto Rico, U.S.A. Nonlinear dynamic analysis of the suspended cable system was carried out with some sensible results that could be useful to the real engineering of LSRT.