Key result
Preprocessing prior to empirical mode decomposition reduces processing time and identifies high-level noise components.
Why the study?
The performance of empirical mode decomposition (EMD) in processing biomedical signals, especially ECG, and the impact of preprocessing and noise identification on EMD outcomes were investigated.
Introducing a preprocessing stage before empirical mode decomposition of ECG signals reduces processing time and aids in noise reduction.
Provides statistical IMF selection for noisy synthetic ECG; leaves open validation in clinical recordings.
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.
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Karagiannis et al. (2010) studied Electrocardiogram (ECG) signals. Empirical mode decomposition (EMD) with preprocessing stage was evaluated on Processing time and variation in the number of intrinsic mode functions (IMFs). The introduction of a preprocessing stage prior to empirical mode decomposition in biomedical time series decreased processing time and allowed identification of high-level noise components.