Abstract
The heart rate variability (HRV) analysis allows the study of the regulation mechanisms of the cardiovascular system, in both normal and pathological conditions, and the power spectral density analysis of the short-term HRV was adopted as a tool for the evaluation of the autonomic function. The Ensemble Empirical Mode Decomposition (EEMD) is an adaptive method generally used to analyze non-stationary signals from non-linear systems. In this work, the performance of the EEMD in the decomposition of the HRV signal in the main spectral components is studied, in a first instance to a synthesized series to calibrate the method and achieve confidence and then to a real HRV database. In conclusion, the results of this work propose the EEMD as useful method for analysis HRV data. The ability of decomposes the main spectral bands and the capability to deal with non-linear and non-stationary behaviors makes the EEMD a powerful method for tracking frequency changes and amplitude modulations in HRV signals generated by autonomic regulation.
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Zelechower, J., Pose, F., Redelico, F., Risk, M. (2021). Comparison of Heart Rate Variability Analysis with Empirical Mode Decomposition and Fourier Transform. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_20
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