Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals
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 leng...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2011-01, Vol.15 (1), p.11-18 |
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| creator | Karagiannis, A Constantinou, P |
| description | 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. |
| doi_str_mv | 10.1109/TITB.2010.2091648 |
| format | Article |
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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. 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| subjects | Algorithms Biomedical signal processing Computer Simulation Cutoff frequency Electrocardiography electrocardiography (ECG) Electrocardiography - methods empirical mode decomposition (EMD) Gaussian noise Humans Signal Processing, Computer-Assisted Signal to noise ratio Studies Time frequency analysis Time series Time series analysis |
| title | Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals |
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