Wavelet transform analysis of heart rate variability during myocardial ischaemia.

Analysis of heart rate variability (HRV) is a valuable, non-invasive method for quantifying autonomic cardiac control in humans. Frequency-domain analysis of HRV involving myocardial ischaemic episodes should take into account its non-stationary behaviour. The wavelet transform is an alternative tool for the analysis of non-stationary signals. Fourteen patients have been analysed, ranging from 40 to 64 years old and selected from the European Electrocardiographic ST-T Database (ESDB). These records contain 33 ST episodes, according to the notation of the ESDB, with durations of between 40s and 12 min. A method for analysing HRV signals using the wavelet transform was applied to obtain a time-scale representation for very low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands using the orthogonal multiresolution pyramidal algorithm. The design and implementation using fast algorithms included a specially adapted decomposition quadrature mirror filter bank for the frequency bands of interest. Comparing a normality zone against the ischaemic episode in the same record, increases in LF (0.0112 +/- 0.0101 against 0.0175 +/- 0.0208 s2 Hz(-1); p<0.1) and HF (0.0011 +/- 0.0008 against 0.00 17 +/- 0.0020 s2 Hz(-1); p<0.05) were obtained. The possibility of using these indexes to develop an ischaemic-episode classifier was also tested. Results suggest that wavelet analysis provides useful information for the assessment of dynamic changes and patterns of HRV during myocardial ischaemia.

Identification of arterial wall dynamics in conscious dogs.

Viscoelastic properties determine the dynamic behaviour of the arterial wall under pulsatile pressure and flow, suggesting time- or frequency-dependent responses to changes in wall stress and strain. The objectives of the present study were: (i) to develop a simplified model to derive simultaneously the elastic, viscous and inertial wall moduli; (ii) to assess Young's modulus as a function of frequency, in conscious, chronically instrumented dogs. Parametric discrete time models were used to characterise the dynamics of the arterial system based on thoracic aortic pressure (microtransducer) and diameter (sonomicrometry) measurements in control steady state and during activation of smooth muscle with the alpha-adrenoceptor agonist phenylephrine (5 microg kg(-1) min(-1), I.V.), in eight conscious dogs. The linear autoregressive model and a physically motivated non-linear model were fitted to the input-output (stress-strain) relationship. The aortic buffering function (complex Young's modulus) was obtained in vivo from the identified linear model. Elastic, viscous and inertial moduli were significantly increased from control state ((44.5 +/- 7.7) x 10(4) Pa; (12.3 +/- 4.7) x 10(4) Pa s; (0.048 +/- 0.028) x 10(4) Pa s(2) ) to active state ((85.3 +/- 29.5) x 10(4) Pa, P < 0.001; (22.4 +/- 8.3) x 10(4) Pa s, P < 0.05; (0.148 +/- 0.060) x 10(4) Pa s(2), P < 0.05). These moduli, obtained using the linear model, did not present significant differences compared with those derived using the non-linear model. In control conditions, the magnitude of the normalised complex Young's modulus was found to be similar to that reported in previous animal studies ranging from 1 to 10 Hz. During vascular smooth muscle activation, this modulus was found to be increased with regard to control conditions (P < 0.01) in the frequency range used in this study. The frequency-dependent Young's modulus of the aortic wall was obtained for the first time in conscious, unsedated dogs. The parametric modelling approach allows us to verify that vascular smooth muscle activation increases the elastic, viscous and inertial moduli with the advantage of being able to track their time evolution. Furthermore, under activation, the aortic wall remains stiff in the physiological frequency range, suggesting the impairment of the arterial buffering function. Experimental Physiology (2001) 86.4, 519-528.