Influence of signals length and noise in power spectral densities computation using Hilbert-Huang Transform in synthetic HRV

María G. Rodríguez; Miguel Altuve; Carlos Lollett; Sara Wong

doi:10.1117/12.2035461

19 November 2013 Influence of signals length and noise in power spectral densities computation using Hilbert-Huang Transform in synthetic HRV

María G. Rodríguez, Miguel Altuve, Carlos Lollett, Sara Wong

Author Affiliations +

Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220U (2013) https://doi.org/10.1117/12.2035461
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico

Abstract

Among non-invasive techniques, heart rate variability (HRV) analysis has become widely used for assessing the balance of the autonomic nervous system. Research in this area has not stopped and alternative tools for the study and interpretation of HRV, are still being proposed. Nevertheless, frequency-domain analysis of HRV is controversial when the heartbeat sequence is non-stationary. The Hilbert-Huang Transform (HHT) is a relative new technique for timefrequency analyses of non-linear and non-stationary signals. The main purpose of this work is to investigate the influence of time series´ length and noise in HRV from synthetic signals, using HHT and to compare it with Welch method. Synthetic heartbeat time series with different sizes and levels of signal to noise ratio (SNR) were investigated. Results shows i) sequence´s length did not affect the estimation of HRV spectral parameter, ii) favorable performance for HHT for different SNR. Additionally, HHT can be applied to non-stationary signals from nonlinear systems and it will be useful to HRV analysis to interpret autonomic activity when acute and transient phenomena are assessed.

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María G. Rodríguez, Miguel Altuve, Carlos Lollett, and Sara Wong "Influence of signals length and noise in power spectral densities computation using Hilbert-Huang Transform in synthetic HRV", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220U (19 November 2013); https://doi.org/10.1117/12.2035461

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