The analysis of heart rate variability, involving changes in the autonomic modulation conditions, demands specific capabilities not provided by either parametric or non-parametric spectral estimation methods. Moreover, these methods produce time-averaged power estimates over the entire length of the record. Recently, empirical mode decomposition and the associated Hilbert spectra have been proposed for non-linear and non-stationary time series. The application of these techniques to real and simulated short-term heart rate variability data under stationary and non-stationary conditions is presented. The results demonstrate the ability of empirical mode decomposition to isolate the two main components of one chirp series and three signals simulated by the integral pulse frequency modulation model, and consistently to isolate at least four main components localised in the autonomic bands of 14 real signals under controlled breathing manoeuvres. In addition, within the short time-frequency range that is recognised for heart rate variability phenomena, the Hilbert amplitude component ratio and the instantaneous frequency representation are assessed for their suitability and accuracy in time-tracking changes in amplitude and frequency in the presence of non-stationary and non-linear conditions. The frequency tracking error is found to be less than 0.22% for two simulated signals and one chirp series.

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Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alphabeta filter to DFA to determine patterns in the power-law behavior that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behavior, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales.

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