Local Penalized Least Squares Combined with the Segment Similarity for ECG Denoising

Electrocardiogram (ECG) has an important reference value in the study of basic cardiac function and pathology. However, ECG signals are easily corrupted by various noises in the acquisition and transmission. Hence, the noise removal in ECG signals is helpful to the diagnosis and analysis. In this paper, both the local sample correlations and nonlocal sample correlations of the ECG signals are utilized to remove noise. According to the local sample correlations, the penalized least squares method is used to remove the noise in the segment. According to the similarity of nonlocal samples, the average of similar segments in different periods can be regarded as a reference estimate for the current signal segment. The effectiveness of the algorithm is verified by adding simulated noises in the MIT-BIH Arrhythmia Database. Compared with some existing methods quantitatively, the signal quality (the improvement in the signal-to-noise ratio, mean square error, and percentage root mean square difference) has been significantly improved. Finally, the proposed method combined with the baseline correction algorithm is applied to raw signals with real noises in ECG-ID Database and gets a favorable visual effect.