Adaptive Noise Reduction and Baseline Drift Correction of ECG Signals Based on Ensemble Empirical Mode Decomposition
In the process of ECG acquisition, the interference of various noises will cause signal distortion and baseline drift, which will affect the accurate judgment of cardiac signal. In this paper, an adaptive algorithm based on set empirical mode de-composition is proposed. Firstly, the intrinsic mode function ( IMF) component is decomposed by ensemble empirical Mode decom-position ( EEMD) for ECG signals containing noise and baseline drift. Then, the IMF components that need to be processed are se-lected. Finally, the low order IMF with noise is processed by adaptive window and the high order IMF with baseline drift is removed to reduce the noise. The experimental results in the MIT-BIH database show that the method based on EEMD achieves good noise re-duction effect. Compared with the adaptive window method based on EMD, the average signal-to-noise ratio is increased by 1.7507, about 13％, under the same myo-electrical noise condition. For the same baseline drift, the average baseline correction rate de-creased by 0.0795, about 14％, compared with the threshold method based on EEMD.
万方数据
维普
