Jl Rojo-Álvarez

Analysis of physiological meaning of detrended Fluctuation Analysis in Heart Rate Variability using a lumped parameter model

Chaos and fractal based measurements, such as detrended fluctuation analysis (DFA), have been widely used for quantifying the heart rate variability (HRV) for cardiac risk stratification purposes. However, the physiological meaning of these measurements is not clear. Given that existing lumped parameter models contain a detailed physiological description of several of the circulatory system regulation processes, we hypothesize that controlled changes in these processes will highlight the physiological basis in DFA indices. We used a detailed lumped parameter model of HRV, introduced earlier. Ten signals were generated in different physiological conditions. DFA coefficients alpha1, alpha2, and the Hurst exponent, were calculated. A clear disruption point was always observed. Modifications in sympatho-vagal activity yielded significant changes in alpha1 when compared to basal, but not in alpha2 or Hurst exponent. Modifications in non-nervous system mediated changes yielded significant differences only for peripheral resistance and heart period, only in alpha1. In conclusion, the analysis of the effect of changes in the regulatory system on the HRV chaotic/fractal indices can be analyzed using detailed lumped parameter models.

Detection and estimation of T wave alternans with matched filter and nonparametric bootstrap test

Though alternans phenomena in the cardiac repolarization phase has been shown to be related to arrhythmgenesis, a definitive estimation method from the T wave of ECG recordings is not yet available. We propose a statistical signal processing scheme which compares the T-wave morphology of even and odd beats by using a running matched filter, in order to increase the signal to noise ratio of the estimation. Given that previously proposed hypothesis tests for alternans detection rely on the knowledge of noise statistical distribution, we also analyzed the usefulness of a nonparametric bootstrap test. Data set composed of 100 ECG recordings included in the Challenge Database were used. Principal Component Analysis was previously made for multilead recordings. Subsequent preprocessing for each available lead consisted of conventional baseline removing, filtering, R-wave detection, exclusion of too noisy segments, T-wave segmentation, and template generation for even and odd beats. The difference between the template and a given beat was obtained by minimizing the absolute error of their comparison with a windowed circular shift. A paired bootstrap resampling test was made for deciding whether the averaged differences between the template and the T-waves were significant compared to the noise level.