A discrete wavelet transformation approach effectively removed both low-frequency baseline wander and high-frequency artifact noise from raw ECG signals.
A discrete wavelet transformation approach effectively corrects baseline wander and reduces high-frequency noise in raw ECG signals.
Electrocardiographic (ECG) analysis plays an important ortant role in safety assessment during new drug development and in clinical diagnosis. The pre-processing of ECG analysis consists of low-frequency baseline wander (BW) correction and high-frequency artifact noise reduction from the raw ECG. We present approaches for BW correction and de-noising based on discrete wavelet transformation (DWT). We estimate the BW via coarse approximation in DWT with recommendations for how to select wavelets and the maximum depth for decomposition ition level. We reduce the high-frequency noise via Empirical Bayes posterior median wavelet shrinkage method with leveldependent ependent and position dependent thresholding values. The methods are applied to a real example. The experimental results indicate that the proposed method can effectively remove both low-and high-frequency noise.
Donghui Zhang (2005) studied ECG analysis. Discrete wavelet transformation (DWT) for baseline wander correction and de-noising was evaluated on Baseline wander correction and noise reduction. A discrete wavelet transformation approach effectively removed both low-frequency baseline wander and high-frequency artifact noise from raw ECG signals.