Maria Gabriella Signorini

Time-variant power spectrum analysis for the detection of transient episodes in HRV signal

A time-variant algorithm of autoregressive (AR) identification is introduced and applied to the heart rate variability (HRV) signal. The power spectrum is calculated from the AR coefficients derived from each single RR interval considered. Time-variant AR coefficients are determined through adaptive parametric identification with a forgetting factor which obtains weighted values on a running temporal window of 50 preceding measurements. Power spectrum density (PSD) is hence obtained at each cardiac cycle, making it possible to follow the dynamics of the spectral parameters on a beat-by-beat basis. These parameters are mainly the LF (low-frequency) and the HF (high-frequency) powers, and their ratio, LF/HF. These together account for the balanced sympatho-vagal control mechanism affecting the heart rate. This method is applied to subjects suffering from transient ischemic attacks. The time variant spectral parameters suggest an early activation of LF component in the HRV power spectrum. It precedes by approximately 1.5-2 min the tachycardia and the ST displacement, generally indicative of the onset of an ischemic episode.<<ETX>>

Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth.

Linear and nonlinear dynamics of heart rate variability after acute myocardial infarction with normal and reduced left ventricular ejection fraction

We analyzed heart rate variability (HRV) in 2 groups of patients after acute myocardial infarction with normal and reduced ejection fraction (EF) by considering both the power of the 2 major harmonic components at low and high frequency and 2 indexes of nonlinear dynamics, namely the 1/f slope and the correlation dimension D2. HRV of patients with a reduced EF was characterized by a diminished RR variance as well as a different distribution of the residual power in all frequency ranges, with lower values of the low-frequency component expressed in both absolute and normalized units, and of the low- to high-frequency ratio. In these patients we also observed a steeper slope of the negative regression line between power and frequency in the very low frequency range. The presence of a smaller fractal dimension was suggested by a lower D2. Thus, in patients after acute myocardial infarction with a reduced EF, the reduction in HRV is associated with a different distribution of the residual power in the entire frequency range, which suggests a diminished responsiveness of sinus node to neural modulatory inputs.

Can power spectral analysis of heart rate variability identify a high risk subgroup of congestive heart failure patients with excessive sympathetic activation? A pilot study before and after heart transplantation.

BACKGROUND AND OBJECTIVES--Autonomic dysfunction seems to be involved in the progression and prognosis of severe congestive heart failure. Parasympathetic activity can still be abnormal 4-8 weeks after haemodynamic improvement by heart transplantation. To identify patients in heart failure with a more pronounced neural derangement and to analyse the changes in sympathetic and parasympathetic activity soon after heart transplantation, spectral indices of heart rate variability were assessed in 30 patients in severe heart failure and in 13 patients after heart transplantation; a group of 15 age-matched subjects served as controls. METHODS AND RESULTS--Heart rate variability was assessed by standard electrocardiography (ECG) in patients in heart failure and by oesophageal ECG in patients after heart transplantation. Compared with controls, the mean RR interval and total power were reduced in heart failure. The 30 patients showed two different patterns of heart rate variability: in 14 no power was detected in the low frequency band (0.03-0.15 Hz) (LF) and total power was mainly concentrated in the high frequency band (0.15-0.45 Hz) (HF), whereas in the remaining 16 patients power in the LF band was increased and power in HF band was reduced compared with the controls. Patients with undetectable LF had a lower mean RR interval and total power (745(25) v 864(36) ms, p < 0.05; 118(16) v 902(202) ms2, p < 0.001), higher concentration of plasma noradrenaline (635(75) v 329(54) pg/ml, p < 0.05), and worse clinical status and prognosis (4 deaths v no deaths at 6 month follow up) than patients with a dominant LF band. In the post-transplant patients both the mean PP interval of the remnant atrium and total power resembled results in the patients with heart failure; in 7 of the 13 post-transplant patients no power was detectable in the LF band: when both HF and LF power were present the results resembled those in the 16 patients in heart failure. CONCLUSIONS--These data suggest that in more advanced stages of congestive heart failure, power spectral analysis of heart rate variability allows identification of a subgroup of patients with higher sympathetic activation and poorer clinical status who are at major risk of adverse events. In the short term after cardiac transplantation the spectral profile of the rhythm variability of the remnant atrium was not improved, suggesting that parasympathetic withdrawal and sympathetic hyperactivity persist, despite the restoration of ventricular function.

Nonlinear indices of heart rate variability in chronic heart failure patients: Redundancy and comparative clinical value

Aims: We aimed to assess the mutual interrelationships and to compare the prognostic value of a comprehensive set of nonlinear indices of heart rate variability (HRV) in a population of chronic heart failure (CHF) patients.

Autonomic Changes During Hypnosis: A Heart Rate Variability Power Spectrum Analysis as a Marker of Sympatho-Vagal Balance

Spectral analysis of beat-to-beat variability in electrocardiography is a simple, noninvasive method to analyze sympatho-vagal interaction. The electrocardiogram is analyzed by means of an automatic, autoregressive modeling algorithm that provides a quantitative estimate of R-R interval variability by the computation of power spectral density. Two major peaks are recognizable in this specter: a low-frequency peak (LF, -0.1 Hz), related to the overall autonomic activity (ortho+parasympathetic) and a high-frequency peak (HF, -0.25 Hz), representative of the vagal activity. The LF/HF ratio is an index of the sympatho-vagal interaction. This technique was applied, using a computer-assisted electrocardiograph, to 10 healthy volunteers (6 high and 4 low hypnotizable subjects as determined by the Stanford Hypnotic Susceptibility Scale, Form C) in randomized awake and neutral hypnosis conditions. Preliminary results indicated that hypnosis affects heart rate variability, shifting the balance of the sympatho-vagal interaction toward an enhanced parasympathetic activity, concomitant with a reduction of the sympathetic tone. A positive correlation between hypnotic susceptibility and autonomic responsiveness during hypnosis was also found, with high hypnotizable subjects showing a trend toward a greater increase of vagal efferent activity than did low hypnotizables.

Assessing nonlinear properties of heart rate variability from short-term recordings: are these measurements reliable?

Several parameters assessing nonlinear properties of heart rate variability (HRV) from short-term (<10 min) laboratory recordings have been proposed so far, but their reliability is unknown. In this study, we addressed this issue analysing a comprehensive set of these indices. In 42 healthy subjects (mean age (min-max): 38 (26-56) years, 21 men) we recorded 5 min of supine ECG in two consecutive days. From RR intervals we computed 11 nonlinear HRV indices, representative of symbolic dynamics, entropy, fractality, predictability, empirical mode decomposition and Poincaré plot families. Absolute reliability was assessed by the 95% limits of random variation and relative reliability was assessed computing the intraclass correlation coefficient (ICC). We found marked differences in the reliability of short-term nonlinear indices of HRV. In the majority of indices, changes in test-retest measurements ranged between about -30% and +50%, indicating good absolute reliability while in the others the change was <-60% and >140%. Relative reliability was substantial (0.6 < ICC < 0.8) in half of the indices, moderate in one and poor in the remaining. Compared to classical linear indices, nonlinear HRV parameters seem more suitable for individual test-retest evaluations but, due to a reduced ICC, they need increased sample size in comparative studies involving two groups of subjects.

Classification of cardiotocographic records by neural networks

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and low-price 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 actual CTG analysis methods, either they consist of the clinician eye inspection or of automatic algorithms. A relevant amount of this unsatisfactory performance resides on the weakness of methods used for classifying fetal conditions and generate a risk alarm during pregnancy. In the paper three neural classifiers are proposed to discriminate among fetal behavioral states and among normal and pathological fetal conditions, on the basis of CTG recordings. All classifiers are fed by indexes extracted from fetal heart rate signal. Results show very promising performance towards the prediction of fetal outcomes on the set of collected FHR signals.

Long-term invariant parameters obtained from 24-h Holter recordings: A comparison between different analysis techniques

Over the last two decades, a large number of different methods had been used to study the fractal-like behavior of the heart rate variability (HRV). In this paper some of the most used techniques were reviewed. In particular, the focus is set on those methods which characterize the long memory behavior of time series (in particular, periodogram, detrended fluctuation analysis, rescale range analysis, scaled window variance, Higuchi dimension, wavelet-transform modulus maxima, and generalized structure functions). The performances of the different techniques were tested on simulated self-similar noises (fBm and fGn) for values of alpha, the slope of the spectral density for very small frequency, ranging from -1 to 3 with a 0.05 step. The check was performed using the scaling relationships between the various indices. DFA and periodogram showed the smallest mean square error from the expected values in the range of interest for HRV. Building on the results obtained from these tests, the effective ability of the different methods in discriminating different populations of patients from RR series derived from Holter recordings, was assessed. To this extent, the Noltisalis database was used. It consists of a set of 30, 24-h Holter recordings collected from healthy subjects, patients suffering from congestive heart failure, and heart transplanted patients. All the methods, with the exception at most of rescale range analysis, were almost equivalent in distinguish between the three groups of patients. Finally, the scaling relationships, valid for fBm and fGn, when empirically used on HRV series, also approximately held.

Quantitative Assessment of Fetal Well-Being Through CTG Recordings: A New Parameter Based on Phase-Rectified Signal Average

Since the 1980s, cardiotocography (CTG) has been the most diffused technique to monitor fetal well-being during pregnancy. CTG consists of the simultaneous recording of fetal heart rate (FHR) signal and uterine contractions and its interpretation is usually performed through visual inspection by trained obstetric personnel. To reduce inter- and intraobserver variabilities and to improve the efficacy of prenatal diagnosis, new quantitative parameters, extracted from the CTG digitized signals, have been proposed as additional tools in the clinical diagnosis process. In this paper, a new parameter computed on FHR time series and based on the phase-rectified signal average curve (PRSA) is introduced. It is defined as acceleration phase-rectified slope (APRS) or deceleration phase-rectified slope (DPRS) depending on the slope sign of the PRSA curve. The new PRSA parameter was applied to FHR time series of 61 healthy and 61 intrauterine growth restricted (IUGR) fetuses during CTG nonstress tests. Performance of APRS and DPRS was compared with 1) the results provided by other parameters extracted from the PRSA curve itself but already existing in the literature, and 2) other clinical indices provided by computerized cardiotocographic systems. APRS and DPRS indices performed better than any other parameter in this study in the distinction between healthy and IUGR fetuses. Our results suggest this new index might reliably contribute to the quality of early fetal diagnosis.