Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction

Electrocardiographic (ECG) analysis plays an important 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 level. We reduce the high-frequency noise via empirical Bayes posterior median wavelet shrinkage method with level-dependent 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