A Multistage Algorithm Design for Electrocardiogram Signal Denoising

Abstract

This work describes a new scheme for denoising noisy electrocardiogram (ECG) signals. In the first step, the noise variance is estimated using the well-known DONOHO’s estimator followed by the wavelet-based baseline wander removing. In the second step, the estimated variance is employed in the adaptive 1D Wiener filter to reduce the additive noise. Next, a Low Pass filter, based on the FFT, is applied on the resulting denoised signal. Furthermore, a cascaded Savitzky–Golay (SG) smoother filter is applied to refine the restoration process. The final step consists in the recovering of the [Formula: see text]-peaks and the surrounding areas. It can be reported that the suggested algorithm is optimal for the additive Gaussian noise and is useful for other types of noises. Both qualitative and quantitative results, achieved from several experimental tests, establish high-quality restoration ability and the efficiency of the proposed method. Thus, when compared to some powerful techniques recently published, the designed algorithm demonstrates very competitive performances.