Application of a modified Hilbert-Huang Transform to autonomic evaluation in metabolic syndrome

The physiological signals are a challenge in terms of identification and processing due to its lack of stationarity and linearity. The autonomic output to the cardiovascular system is classically quantified by analysis of heart rate variability. The Fast Fourier Transform (FFT) and Discrete Wavelet Transform applied to the cardiac signals showed limitations in terms of time and frequency resolutions. With this work we propose the application of a Modified Hilbert-Huang Transform for tachogram and systogram evaluation with the introduction of a new method to smooth fluctuations endpoint. This work presents a comparative study using FFT and Hilbert-Huang methodologies. It is known that patients and animal models with diseases involving sympathoexcitation, eg.: Metabolic Syndrome (MetS), show increases in low frequencies band. Therefore, these mathematical tools are evaluated by analyzing experimental data from two animal species: MetS-rabbits (n=6) and MetS-rats (n=6) and their respective lean age and sex-matched controls (n=6 for each group). Modified Hilbert-Huang Transform analysis showed a sympathoexcitatory condition in MetS comparing to controls both in a rabbit and in a rat model of MetS. These significant differences on sympathetic tone were also confirmed by FFT but, the parasympathetic activity, was similar between normal and MetS animals. Results and its physiological interpretation clearly show the suitability of the modified Hilbert-Huang Transform as an alternative methodology for the dynamic assessment of the autonomic nervous system in MetS.