Title :
Ensemble empirical mode decomposition based ECG noise filtering method
Author :
Chang, Kang-Ming
Author_Institution :
Dept. of Photonics & Commun. Eng., Asia Univ., Wufeng, Taiwan
Abstract :
Electrocardiogram is often corrupted by various noises, such as high-frequency muscle contraction. In this study, ensemble empirical mode decomposition (EEMD) was used for ECG noise reduction. Gaussian noise was applied and the average (ensemble) intrinsic mode function (IMF) was used for ECG reconstruction. Three high frequency ECG noises; muscle contraction, 50 Hz power line interferences and Gaussian noise were examined. Mean square error (MSE) between filtered ECG and clean ECG was used as a reconstruction performance index. Results showed that the first two IMF levels were deleted owing to their noise components, while the other ensemble IMF constituted clean ECG components for ECG reconstruction. The MSE of EEMD is lower than EMD and IIR filter on these three noise types due to the reduction of mode-mixing effect between separate IMFs.
Keywords :
Gaussian noise; IIR filters; electrocardiography; filtering theory; mean square error methods; signal denoising; ECG noise filtering method; ECG noise reduction; ECG reconstruction; Gaussian noise; IIR filter; electrocardiogram; ensemble empirical mode decomposition; high-frequency muscle contraction; infinite impulse response filters; intrinsic mode function; mean square error; Electrocardiography; Filter bank; Finite impulse response filter; IIR filters; Noise; Noise reduction; Electrocardiogram (ECG); Ensemble Empirical mode decomposition (EEMD); Noise reduction;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5581064