S. Cerutti

Power spectrum analysis of cardiovascular variability monitored by telemetry in conscious unrestrained rats

Beat-to-beat variability of arterial pressure and heart period (R-R) was studied in eight conscious freely-moving adult male rats in which telemetric recordings of arterial pressure, ECG and respiratory movements were obtained under unrestrained and unstressed conditions. The beat-to-beat time series of these signals (systolic arterial pressure, diastolic arterial pressure and R-R) were analyzed, in the frequency domain, using autoregressive spectral analysis in order to detect and quantify the rhythmic components. In basal conditions, the systolic arterial pressure variability spectrum was characterized by three major spectral components which had central frequencies respectively of 0.08 +/- 0.03 Hz (very low frequency), 0.43 +/- 0.02 Hz (low frequency) and 1.36 +/- 0.19 Hz (high frequency). Similar rhythmic components were found in R-R signal variability. The very low frequency component included a higher percentage of total power in R-R variability spectrum (75.3%) than in systolic arterial pressure variability spectrum (58.4%). The low frequency component was more pronounced in both systolic and diastolic arterial pressure variability spectra. The high frequency component of R-R, systolic and diastolic arterial pressure was synchronous with respiration. Cross-spectral analysis revealed a high statistical coherence between R-R and arterial pressure variabilities in all the three frequency bands. An alpha-adrenergic blocker (phentolamine) specifically abolished the low frequency components of systolic and diastolic arterial pressure variability spectra, thus suggesting that low frequency is a marker of sympathetic modulation of vasomotor activity. The low frequency component of R-R variability spectrum was also markedly blunted. We suggest that cardiovascular variability signals, (R-R, systolic and diastolic arterial pressure) are composed almost of two main rhythms linked to respiration and vasomotor activity. These rhythms can be quantified in conscious unrestrained rats by using telemetry and spectral analysis. This approach seems to offer a new powerful tool for pharmacological studies in conscious small animals.

Power spectrum analysis contribution to the detection of cardiovascular dysautonomia in multiple sclerosis

In multiple sclerosis (MS) autonomic cardiovascular dysfunction is an uncommon, but potentially dangerous event, to which studies of spectral analysis of heart rate variability have not been applied, yet. Material and methods – We studied 16 patients with definite MS (11 women and 5 men, mean age 30.3 ± 7.4 yrs., mean EDSS 2.06±1.42) and 16 sex‐ and age‐matched healthy controls. Besides cardiovascular reflex tests (valsalva manoeuvre, deep breathing, lying to standing, Blood Pressure response to standing and sustained handgrip), each underwent spectral analysis of the R‐R interval short‐term variability at rest and after tilting, to detect three components: very low frequency (VLF), low frequency (LF) and high frequency (HF). A recent brain MRI was obtained from patients, to compare plaque characteristics with spectral parameters. Results – At cardiovascular reflexes, only four patients (25%) showed an impairment, mostly of a mild degree. VLF and LF at rest were lower in MS subjects than in controls (p<0.01). No significant correlation was found between spectral parameters and lesion area or localization as detected on MRI. Conclusions – Spectral analysis could usefully flank reflex tests to detect autonomic subclinical cardiovascular abnormalities.

Single-sweep analysis using an autoregressive with exogenous input (ARX) model

Single-sweep visual evoked potential analysis would be useful in clinical electro-physiology practice because it would make possible the evaluation of transient phenomena, but recording single-sweep visual evoked potentials is difficult because of the low signal-noise ratio. To increase this ratio we used a filter based on an autoregressive with exogenous input model. We studied a group of 12 diabetic patients matched with a control group of 14 normal subjects. The model, in most cases, allowed us to extrapolate the P100 component from each single sweep of visual evoked potential. The visual evoked potential values obtained by means of averaging were not significantly different in the groups studied, but single-sweep analysis showed different distribution of the P100 component amplitude. The preliminary results of our study evidenced differences in the amplitude and latency distribution of normal and diabetic subjects, thus confirming the power of this new technique and its ability to obtain some information that is masked by the averaging method.

Reliability of the measurement of RT variability

The variability of the interval between the R peak and the apex of T wave has a power 2-3 orders of magnitude smaller than that of RR interval variability therefore, adequate recording, pre-processing and detection procedures are required to avoid artefacts. Emphasis is posed on the dramatic differences occurring between different leads, which are explained as artefacts induced by changes in the cardiac electrical axis. A satisfactory compensation appears to be provided by the modulus signal from three orthogonal leads. So, the low frequency (LF) and high frequency (HF) components were clearly recognised in 14 normal (N) subjects and in 12 patients 2 weeks after myocardial infarction (MI). This was confirmed by the cross-spectrum between the RT and the RR series. In N, HF (36.7/spl plusmn/23.1 nu.) was predominant over LF (25.4/spl plusmn/14.5), while in MI the ratio was inverted (LF=37.0/spl plusmn/23.3, HF=25.6/spl plusmn/13.9).<<ETX>>

Chaotic characteristics of heart rate variability signal in newborns

Heart rate variability signal regulation is influenced by several mechanisms. Non-linear dynamic modelling approach seems to be a promising metho in order to extract information about the long period regulation and to measure the chaos level in biological signals. For deterministic chaotic systems the state-vector describes a strange attractor with a fractal (non-integer) dimension D. We have estimated fractal dimension on HRV signal of 18 newborn infants (5 from caesarean cut, 13 from spontaneous delivery) using the Self-Similarity algorithm. All the newborns from caesarean delivery manifest a lower degree of chaos (D=5.09 STD=1.9) in respect to the normal ones (D=9.2 STD=5.93).

Autoregressive time-variant analysis of transient ischemic attacks

This paper introduces a method of analysis of short-term heart rate variability control during the evolution of ischemic transient attacks by autoregressive (AR) spectral identification. Signals are obtained from ECG Holter recordings of patients with two different ischemic pathologies: Syndrome X, which is believed to be due to coronary microvascular dysfunction, and Chronic Stable Angina. Parameters such as the power and the carrier frequency of the VLF, LF and HF bands are considered. Some of these parameters may describe characteristic different behaviors of the cardiovascular control system during the two ischemic pathologies, in the attempt to keep a stable neurovegetative state.

Low and high frequency non-linear interactions in the sympathetic discharge: contribution of spinal centers

When biological oscillators interact as a result of nonlinear mechanisms several phenomena such as period doubling, phase locking, complex and irregular behaviors may occur. These behaviors depend on the degree of the coupling among the rhythmic generators and on the amplitude and frequency of the relevant oscillations. In decerebrate artificially ventilated cats low (around 0.1 Hz) and high (around 0.3 Hz) frequency rhythms present in the preganglionic efferent sympathetic activity directed to the heart are affected by forced periodical ventilation. These interactions result in various ratios between sympathetic discharge and ventilatory cycles (1:1, 1:2, 1:3, 1:4 and lower). After spinal section, as a consequence of the reduction of the coupling between central oscillators, external ventilation and sympathetic activity, irregular behaviors including broad band patterns, weak low frequency rhythms and period-three periodicities are classified.<<ETX>>

Time-variant parametric model for the 24-H evaluation of the closed-loop /spl alpha/-baroreceptive gain

Spectral analysis of RR scries. obtained from ECG. and systolic arterial pressure ( S A P ) series. obtained froni continuos recording of pressure signal. is now a well established tool for the quantification of Autonomic Nervous System status in controlling the cardiovascular parameters. In particular bivariate parametric models are able to describe mutual relationships between RR and S A P signals, and to calculate the a-baroreceptive gain evaluating how oscillations in pressure systolic values can affect oscillation in heart rate. in the frequency bands in which the signals present high coherence values (LF and HF bands). In this paper continuos cstraction of tx-gain is obtained by making the bivariate iiiodel time-variant and able to follow and describe transient phenomena. The bivariate model is updated recursively according to the dynamic variations of the signals. The algorithm is employed for the beat-to-beat evaluation of a-parameter during 24-hour recording, and the determination of the circadian modifications of the spectral parameters is analyzed with beat-to-beat disc ret iza t ion.

Time-variant AR spectral estimation in the study of vasovagal syncope

The AutoRegressive (AR) spectral estimation is implemented in a recursive way with a forgetting factor w both in the classical RLS form, with a constant w value, and in the Fortescue variant with w changing with time, according to the changing characteristics of the signals. The time-variant AR algorithm is here employed, in the study of heart rate and blood pressure variability signals obtained during syncope episodes. The spectral parameters (LF, HF powers, LF/HF ratio), which are able to quantify the sympatho-vagal balance in the assessing of the heart rate and blood pressure values, are evaluated on a beat-to-beat basis, in order to obtain more information about the role played by the autonomie nervous system during these episodes.

Time-variant estimation of the spectral parameters of Heart Rate Variability

Recursive Auto-Regressive (AR) identification algorithms, applied to the study of Heart rate Variability (HRV), allow to investigate transient biological phenomena. Each time the AR parameters are updated, the positions of poles must be evaluated in order to quantify frequency and power of spectral peaks by means of a spectral decomposition based on the residual integration method. We describe a recursive method for pole tracking that estimate the new pole positions on the basis of the AR parameters variations, in a more efficient way than traditional factorization algorithms. The method is suitable for an on-line monitoring of traditional HRV parameters, which measure the symphato-vagal balance elicited by the control mechanisms of cardiovascular system.