Identification of development and autonomic nerve activity from heart rate variability in preterm infants

Heartbeat intervals, which are determined basically by regular excitations of the sinoatrial node, show significant fluctuation referred to as the heart rate variability (HRV). The HRV is mostly due to nerve activities through the sympathetic and parasympathetic branches of the autonomic nervous system (ANS). In recent years, it has been recognized that the HRV shows a greater complexity than ever expected, suggesting that it includes much information about ANS activities. In this study, we investigated relationship between HRV and development in preterm infants. To this end, heartbeat intervals were continuously recorded from 11 preterm infants in NICU. The recording periods were ranging from several days to weeks depending on the individuals. The HRV at various ages was then characterized by several indices. They include power spectrum as well as the mean and standard deviation of the series. For the power spectrum, the low-frequency band power (LF), high-frequency band power (HF), LF/HF (the ratio between LF and HF), beta (scaling exponent of the spectrum) were estimated. The detrended fluctuation analysis (DFA) was also employed to obtain short- and long-range scaling exponents. Each of these indices showed a correlation with the age. We showed that the long-range scaling exponent, derived from the DFA, was most significantly correlated with the age, suggesting that it could be a robust index to characterize the development of preterm infants.     

A parametric family of correlation structures for the analysis of longitudinal data.

In epidemiological settings, we are often faced with numerous short time series, and a parsimonious parametrization of the correlation structure is desired in order to optimize the efficiency of the estimation procedure. We propose a damped exponential correlation structure for modeling multivariate Gaussian outcomes. The correlation between two observations separated by s units of time is modeled as gamma s theta, where gamma is the correlation between elements separated by one s-unit, and theta is a damping parameter. For (theta = 0), (theta = 1), and theta----infinity), the correlation structures of compound symmetry, first-order autoregressive, and first-order moving average processes are obtained. Although the AR(2) dependency structure, and the combination of random effects and AR(1) errors are not special cases of the proposed parametric family, these structures can be well approximated within the family for short time series. Maximum likelihood methods for parameter estimation and interpretations of intermediate models (0 less than theta less than 1) are discussed in the context of modeling pulmonary function in an adult population in The Netherlands and T-cell subsets in homosexual men infected with human immunodeficiency virus Type I.     

Influence of sympathetic vascular regulation on heart-rate scaling structure: spinal cord lesion as a model of progressively impaired autonomic control.

Estimation of self-similarity is a promising tool for quantifying alterations in cardiovascular dynamics. To evaluate the as yet unexplored influence of sympathetic vascular regulation on the scaling exponent, namely on the parameter characterizing self-similarity, we studied patients with a spinal cord injury as a model of progressively impaired vascular control. We considered 24 able-bodied subjects (AB) and 23 paraplegics with increasing lesion levels: between T(12) and L(4) (n=7); T(5) and T(11) (n=9); and C(6) and T(4) (n=7). We recorded the heart rate in three conditions characterized by increasing sympathetic activation: supine (SUP), sitting (SIT) and exercise (EXE). We calculated the scaling exponent by detrended fluctuation analysis (H(DFA)). Sympathetic activation had different effects on H(DFA), depending on the lesion level. H(DFA) tended to decrease in AB from SUP (0.85+0.02; mean+SEM) and SIT (0.84+0.02) to EXE (0.79+0.02). It remained constant in the T(12)-L(4) group (0.92+0.04, 0.94+0.05 and 0.94+0.04, respectively), while it increased significantly in the T(5)-T(11) group (0.88+0.07, 0.94+0.05, 1.00+0.08) and increased even more in the C(6)-T(4) group (0.83+0.07, 0.91+0.05, 1.06+0.06). Results suggest that heart-rate self-similarity depends on vascular sympathetic control, because it is altered by spinal-cord lesions, even when cardiac neural control is intact.     

生理信号回归问题的一种解法

一自适应在线过程可通过Kalman滤波表示为时间变多变量的的自回归模型。时变模型的参数的估计能被用以计算线相关的瞬时测量,此方法对生理信号的分析非常有用。本文着重讨论了在呼吸运动,心率波动,血压的分析中的应用。     

Identification of EEG patterns occurring in anesthesia by means of autoregressive parameters.

In EEG analysis an automatic pattern recognition is of interest. In this paper the usefulness of autoregressive parameters to classify EEG segments recorded during anesthesia is examined. Assuming that the AR parameters are multivariate normally distributed, parametric methods of discriminant analysis can be applied. The results show that AR parameters have high discriminating power and that the lowest error classification rate (smaller than 3%) is obtained by using quadratic discriminant functions. Consequently autoregressive parameters are efficient for classifying EEG segments into general stages of anesthesia.     

A simplified two-component model of blood pressure fluctuation

We propose a simple moving-average (MA) model that uses the low-frequency (LF) component of the peroneal muscle sympathetic nerve spike rate (LF(spike rate)) and the high-frequency (HF) component of respiration (HF(Resp)) to describe the LF neurovascular fluctuations and the HF mechanical oscillations in systolic blood pressure (SBP), respectively. This method was validated by data from eight healthy subjects (23-47 yr old, 6 male, 2 female) during a graded tilt (15 degrees increments every 5 min to a 60 degrees angle). The LF component of SBP (LF(SBP)) had a strong baroreflex-mediated feedback correlation with LF(spike rate) (r = -0.69 +/- 0.05) and also a strong feedforward relation to LF(spike rate) [r = 0.58 +/- 0.03 with LF(SBP) delay (tau) = 5.625 +/- 0.15 s]. The HF components of spike rate (HF(spike rate)) and SBP (HF(SBP)) were not significantly correlated. Conversely, HF(Resp) and HF(SBP) were highly correlated (r = -0.79 +/- 0.04), whereas LF(Resp) and LF(SBP) were significantly less correlated (r = 0.45 +/- 0.08). The mean correlation coefficients between the measured and model-predicted LF(SBP) (r = 0.74 +/- 0.03) in the supine position did not change significantly during tilt. The mean correlation between the measured and model-predicted HF(SBP) was 0.89 +/- 0.02 in the supine position. R(2) values for the regression analysis of the model-predicted and measured LF and HF powers indicate that 78 and 91% of the variability in power can be explained by the linear relation of LF(spike rate) to LF(SBP) and HF(Resp) to HF(SBP). We report a simple two-component model using neural sympathetic and mechanical respiratory inputs that can explain the majority of blood pressure fluctuation at rest and during orthostatic stress in healthy subjects.     

大鼠心率变异性频谱中高频成分的中枢机理分析

本文探讨心率变异性（ＨＲＶ）频谱中高频成分的中枢机理。对正常ＳＤ大量给予不同频率的人工通气并电刺激延髓疑核,观察ＨＲＶ频谱的改变,记录与呼吸节律同步的延髓头端腹外侧区（ｒＶＬＭ）及其周围区神经元细胞外单位放电,对ＨＲＶ和放电变异性进行相干函数分析。结果显示：（１）ＨＲＶ的高频成分的中心频率随着人工通气频率的增加而增加,呈高度线性相关,（ｒ＝０．８３,Ｐ〈０．０００１）;（２）对ｒＶＬＭ及其周围区与     

Doppler echo evaluation of pulmonary venous-left atrial pressure gradients: human and numerical model studies

The simplified Bernoulli equation relates fluid convective energy derived from flow velocities to a pressure gradient and is commonly used in clinical echocardiography to determine pressure differences across stenotic orifices. Its application to pulmonary venous flow has not been described in humans. Twelve patients undergoing cardiac surgery had simultaneous high-fidelity pulmonary venous and left atrial pressure measurements and pulmonary venous pulsed Doppler echocardiography performed. Convective gradients for the systolic (S), diastolic (D), and atrial reversal (AR) phases of pulmonary venous flow were determined using the simplified Bernoulli equation and correlated with measured actual pressure differences. A linear relationship was observed between the convective (y) and actual (x) pressure differences for the S (y = 0.23x + 0.0074, r = 0.82) and D (y = 0.22x + 0.092, r = 0.81) waves, but not for the AR wave (y = 0. 030x + 0.13, r = 0.10). Numerical modeling resulted in similar slopes for the S (y = 0.200x - 0.127, r = 0.97), D (y = 0.247x - 0. 354, r = 0.99), and AR (y = 0.087x - 0.083, r = 0.96) waves. Consistent with numerical modeling, the convective term strongly correlates with but significantly underestimates actual gradient because of large inertial forces.     

Parametric representation of normal breath sounds.

The spectral content of normal tracheal and chest wall breath sounds has been calculated using the fast Fourier transform (FFT) (J. Appl. Physiol. 50: 307-314, 1981). Parameter estimation methods, in particular autoregressive (AR) modeling, are alternative techniques for measuring lung sounds. The outcome of AR modeling of 38 complete breaths picked up simultaneously over the chest walls and tracheae of five normal males was evaluated. The sounds were treated as noise, bounded by a quasi-periodic envelope generated by the cyclic action of breathing, thus causing the sounds to become inherently nonstationary. Normalization of the sounds to their corresponding variance envelopes eliminated the nonstationarity, an important requirement for most signal-processing methods. Subsequently, the AR model order was sought using formal criteria. Orders 6-8 were found to be suitable for normal chest wall sounds, whereas tracheal sounds required at least orders 12-16. Using orders 6 and 12, we compared the prominent spectral features of chest wall and tracheal sounds calculated by AR with those found in the spectra calculated by FFT. The polar representation of the AR roots, calculated from the AR coefficients, showed that normal lung sounds from a group of individuals are characterized by a low variability, suggesting that this method may provide an alternative representation of the sounds. The data presented here show that normal lung sounds, when measured in the frequency domain by either FFT or AR modeling, have a characteristic pattern that is independent of the analysis method.     

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.     

Neurohormonal profile of patients with heart failure and diabetes

Background. Neurohormonal activation is generally recognised to play an important role in the pathophysiology, prognosis and treatment of chronic heart failure (HF). While the number of patients with diabetes increases, little if anything is known about neurohormonal activation in HF patients with diabetes. Methods. The study population consisted of 371 patients with advanced HF who were enrolled in a multicentre survival trial. Ten different plasma neurohormones were measured (noradrenaline, adrenaline, dopamine, aldosterone, renin, endothelin, atrial natriuretic peptide [ANP], N-terminal (pro)ANP, brain natriuretic peptide [BNP] and N-terminal (pro)BNP. Comparisons were made between patients with diabetes (n=81) and those without (n=290). Results. At baseline, the two groups were comparable regarding age (mean 68 years), left ventricular ejection fraction (23%), severity and aetiology of HF, while body weight was higher in those with diabetes (77.4 vs. 74.2 kg, p=0.04). Most plasma neurohormones were similar between groups, but patients with diabetes had higher values of BNP (94 vs. 47 pmol/l, p=0.03), while a similar trend was observed for N-terminal (pro)BNP (750 vs. 554 pmol/l, p=0.10). During almost five years of follow-up, 51/81 patients with diabetes died (63%), as compared with 144 of 290 non-diabetic patients (50%) who died (p=0.046). Natriuretic peptides and noradrenaline were the most powerful predictors of mortality in both diabetic and non-diabetic HF patients. Conclusion. HF patients with diabetes have higher (N-terminal (pro)) BNP levels than non-diabetic patients, while other neurohormones are generally similar. Natriuretic peptides are also good prognostic markers in diabetic HF patients. (Neth Heart J 2010;18:190-6.)     

A close relationship between primary nucleotides sequence structure and the composition of functional genes in the genome of prokaryotes

Comparative genomics is an essential tool to unravel how genomes change over evolutionary time and to gain clues on the links between functional genomics and evolution. In prokaryotes, the large, good quality, genome sequences available in public databases and the recently developed large-scale computational methods, offer an unprecedent view on the ecology and evolution of microorganisms through comparative genomics. In this work, we examined the links among genome structure (i.e., the sequential distribution of nucleotides itself by detrended fluctuation analysis, DFA) and genomic diversity (i.e., gene functionality by Clusters of Orthologous Genes, COGs) in 828 full sequenced prokaryotic genomes from 548 different bacteria and archaea species. DFA scaling exponent α indicated persistent long-range correlations (fractality) in each genome analyzed. Higher resolution power was found when considering the sequential succession of purine (AG) vs. pyrimidine (CT) bases than either keto (GT) to amino (AC) forms or strongly (GC) vs. weakly (AT) bonded nucleotides. Interestingly, the phyla Aquificae, Fusobacteria, Dictyoglomi, Nitrospirae, and Thermotogae were closer to archaea than to their bacterial counterparts. A strong significant correlation was found between scaling exponent α and COGs distribution, and we consistently observed that the larger α the more heterogeneous was the gene distribution within each functional category, suggesting a close relationship between primary nucleotides sequence structure and functional genes composition.     

Time–frequency analysis of variabilities of heart rate, systolic blood pressure and pulse transit time before and after exercise using the recursive autoregressive model

Time–frequency (T–F) analysis is often used to study the non-stationary cardiovascular oscillations such as heart rate and blood pressure variabilities in dynamic situations. This study intends to use the T–F recursive autoregressive technique to investigate variability in pulse transit time (PTT), which is a cardiovascular parameter of emerging interest due to its potential to estimate blood pressure non-invasively, continuously and without a cuff. Recent studies suggest that PTT is not only related to systolic blood pressure (SBP) but also to heart rate. Therefore, in this study, variability of PTT is analyzed together with the variabilities of R–R interval (RRI) from electrocardiogram and beat-to-beat SBP on 9 normotensive subjects before and shortly after three successive bouts of treadmill exercise. The results showed that both low frequency (LF) and high frequency (HF) components were found in the spectra of RRI, SBP and PTT in the 5-min recordings collected before and after exercise. Compared to the baseline, a decrease in the power of the HF component of RRI followed by an increase in its LF component indicated firstly a vagal withdrawal and then sympathetic activity enhancement after successive bouts of exercise. On the other hand, although changes in the LF and HF components of PTT were more similar to those of SBP than of RRI, the LF/HF ratio of SBP was almost 4 times higher than that of PTT. Based on the results, it is therefore suggested that the relationship between SBP and PTT is frequency-dependent.     

Heart rate variability signal processing: A quantitative approach as an aid to diagnosis in cardiovascular pathologies

The heart rate variability (HRV) signal carries important information about the systems controlling heat rate and blood pressure, mainly elicited by autonomic nervous system (sympathetic and parasympathetic) controls. The present paper illustrates methods of HRV signal processing by using autoregressive (AR) modeling and power spectral density estimate. The information enhanced in this way seems to be particularly sensitive in discriminating various cardiovascular pathologies (hypertension, myocardial infarction, diabetic neuropathy, etc.). This method provides a simple non-invasive analysis, based on the processing of spontaneous oscillations in heart rate. Particular emphasis is directed to the algorithms used and to their direct application by using proper computerized techniques: only a few paradigmatical examples will be illustrated as preliminary results.     

Cardiovascular autonomic function correlates with the response to aerobic training in healthy sedentary subjects

Individual responses to aerobic training vary from almost none to a 40% increase in aerobic fitness in sedentary subjects. The reasons for these differences in the training response are not well known. We hypothesized that baseline cardiovascular autonomic function may influence the training response. The study population included sedentary male subjects (n = 39, 35 +/- 9 yr). The training period was 8 wk, including 6 sessions/wk at an intensity of 70-80% of the maximum heart rate for 30-60 min/session. Cardiovascular autonomic function was assessed by measuring the power spectral indexes of heart rate variability from 24-h R-R interval recordings before the training period. Mean peak O2 uptake increased by 11 +/- 5% during the training period (range 2-19%). The training response correlated with age (r = -0.39, P = 0.007) and with the values of the high-frequency (HF) spectral component of R-R intervals (HF power) analyzed over the 24-h recording (r = 0.46, P = 0.002) or separately during the daytime hours (r = 0.35, P = 0.028) and most strongly during the nighttime hours (r = 0.52, P = 0.001). After adjustment for age, HF power was still associated with the training response (e.g., P = 0.001 analyzed during nighttime hours). These data show that cardiovascular autonomic function is an important determinant of the response to aerobic training among sedentary men. High vagal activity at baseline is associated with the improvement in aerobic power caused by aerobic exercise training in healthy sedentary subjects.     

A method of neuronal spike train study based on autoregressive analysis

The method of autoregressive (AR) analysis for neuronal spike trains (NST) is proposed in the paper. The AR model and the Green's function as well as the power spectral density function are used to process and analyse the neuronal interspike interval (ISI) sequences of cat's first somatosensory area of cortex (SI area) under various situations. With these methods the characteristics of the ISI sequence such as the AR order and parameters, memory property, correlativity and periodicity etc. can be extracted.     

Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson's Disease: Stitching Together Short Gait Trials

Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ( strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents (DFA) and (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning and . Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true . The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.     

Correlation properties of heartbeat dynamics

In this study, we investigate correlation properties of fluctuations in heart interbeat (RR) time series in a broad range of physiological and pathological conditions. Using detrended fluctuation analysis (DFA) method we determined short-term (alpha (1)) and long-term (alpha (2)) scaling exponent. In addition, we calculated standard deviation of RR intervals (SDRR) as the simplest variability measure. We found that the difference between alpha (1) and alpha (2) is related to RR interval length. At the shortest RR intervals, which correspond to extreme physiological and pathological conditions, we found the highest reduction of variability and the biggest difference between scaling exponents. In this case, DFA reveals a white noise over short scales (alpha (1 )about 0.5) and strongly correlated noise over large scales (alpha (2) about 1.5). With an increase in RR interval, accompanied by increased variability (increase in parasympathetic control), the difference between alpha (1) and alpha (2) decreases. The difference between scaling exponents disappeared in a state of efficient autonomic control. We suggest that the complexity in heart rhythm is achieved through coupling between intrinsically controlled heart rhythm and autonomic control, and that the model of stochastic resonance mechanism could be applied to this system.     

Dynamic regulation of heart rate during acute hypotension: new insight into baroreflex function

To examine the dynamic properties of baroreflex function, we measured beat-to-beat changes in arterial blood pressure (ABP) and heart rate (HR) during acute hypotension induced by thigh cuff deflation in 10 healthy subjects under supine resting conditions and during progressive lower body negative pressure (LBNP). The quantitative, temporal relationship between ABP and HR was fitted by a second-order autoregressive (AR) model. The frequency response was evaluated by transfer function analysis. Results: HR changes during acute hypotension appear to be controlled by an ABP error signal between baseline and induced hypotension. The quantitative relationship between changes in ABP and HR is characterized by a second-order AR model with a pure time delay of 0.75 s containing low-pass filter properties. During LBNP, the change in HR/change in ABP during induced hypotension significantly decreased, as did the numerator coefficients of the AR model and transfer function gain. Conclusions: 1) Beat-to-beat HR responses to dynamic changes in ABP may be controlled by an error signal rather than directional changes in pressure, suggesting a "set point" mechanism in short-term ABP control. 2) The quantitative relationship between dynamic changes in ABP and HR can be described by a second-order AR model with a pure time delay. 3) The ability of the baroreflex to evoke a HR response to transient changes in pressure was reduced during LBNP, which was due primarily to a reduction of the static gain of the baroreflex.