Application of empirical mode decomposition to heart rate variability analysis

Abstract

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.

Empirical mode decomposition analysis of HRV data from patients undergoing local anaesthesia (brachial plexus block)

“…Various studies (Echeverría et al 2001, Souza Neto et al 2004 can be found in the literature that have used the EMD technique for the analysis of HRV signals; however, in this study a more thorough approach has been used. In this study a new method based on CF and SDSE has been proposed for assigning the IMF components into the HF and the LF bands of the HRV signal.…”

“…In this study a new method based on CF and SDSE has been proposed for assigning the IMF components into the HF and the LF bands of the HRV signal. In previous studies (Echeverría et al 2001, Souza Neto et al 2004 the IMF components were usually split into the HF and the LF bands by manually observing their frequency contents. The results produced in this way could be quite subjective.…”

Powerline noise elimination in neural signals via blind source separation and wavelet analysis

“…As expected, each consecutive IMF had characteristically lower frequencies. It can also be observed that, although a summation of the IMFs and the residue results in the input signal, the IMFs and the residue extracted do not have the slightest propensity towards linearity, stationarity nor time-invariance (Echeverria et al, 2001;Huang and Shen, 2005;Shi and Law, 2007). This approach to signal decomposition is effectively dynamic and is void of a priori assumptions of the characteristics of the input data.…”

Biomedical Time Series Processing and Analysis Methods: The Case of Empirical Mode Decomposition

“…Literature references' variety reveals the extensive range of EMD applications in several areas of the biomedical engineering field. Particularly there are publications concerning the application of EMD in the study of Heart Rate Variability (HRV) [8], analysis of respiratory mechanomyographic signals [9], ECG enhancement artifact and baseline wander correction [10], R-peak detection [11], Crackle sound analysis in lung sounds [12] and enhancement of cardiotocograph signals [13]. The method is employed for filtering electromyographic (EMG) signals in order to perform attenuation of the incorporated background activity [14].…”