Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals

Abstract

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

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

“…The flowchart of the proposed methodology is applied on both simulated and real record ECG time series and the branch outputs are compared in order to evaluate the preprocessing stage and the effect in EMD performance. Results of the proposed methodology are provided in [22] and [17] with more details concerning the pre-processing stage which is implemented as typical filters and the way this stage affects the output of the empirical mode decomposition application on the simulated and real biomedical time series. Empirical mode decomposition performance is checked in terms of statistical significance of the IMF set produced, the variation of the IMF set length as a function of time series length and SNR and the computation time.…”

“…In both graphs EMD performance in terms of computation time is worst compared to the corresponding performance of ECG time series preprocessed with the suitable filter. Overall, EMD performance of LP 1 highlights the important role of suitable preprocessing stage selection [22]. …”

A comparative study of denoising sEMG signals

“…Recently, novel methods such as wavelet and empirical mode decomposition (EMD) have been successfully applied to remove noise from sEMG signals [11][12][13][14]. EMD, which has a wide application area in science and engineering, is a new signal decomposition method for analyzing data [15,16].…”

Depicting Respiratory Characteristics of Blood Pressure Signal by Using Empirical Mode Decomposition

“…The final set of ensemble IMFs is obtained by averaging each corresponding IMF decomposed from different mixtures. The EEMD has been used in several medical studies [24][25][26][27], though EEMD is insufficient for characterization of ABP signal owing to its non-zero residue. The residue of added white noise could be removed by adding positive and negative white noises respectively as two mixtures of the source signal and added white noise, which is called complementary EEMD (CEEMD).…”