Electrocardiograph signal denoising based on sparse decomposition

• Published in: Healthcare technology letters Vol. 4; no. 4; pp. 134 - 137
• Main Authors: Zhu, Junjiang, Li, Xiaolu
• Format: Journal Article
• Language: English
• Published: England The Institution of Engineering and Technology 01.08.2017; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Asymmetry; Cardiac arrhythmia; Dictionaries; ECG signal denoising; electrocardiography; iterative methods; linear denoising method; Linear programming; matching pursuit algorithm; medical signal processing; Methods; MIT-BIH arrhythmia database; myoelectric interference; Noise; signal denoising; Signal processing; sparse decomposition; sparse-based method; Symmetry; time-frequency analysis; Wavelet transforms; signal denoising; medical signal processing; iterative methods; ECG signal denoising; time-frequency analysis; MIT-BIH arrhythmia database; sparse decomposition; myoelectric interference; sparse-based method; matching pursuit algorithm; electrocardiography; linear denoising method
• ISSN: 2053-3713; 2053-3713
• DOI: 10.1049/htl.2016.0097

Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed. Then the model is solved by matching pursuit algorithm. In order to get better results, four kinds of dictionaries are investigated with the ECG signals from MIT-BIH arrhythmia database, compared with wavelet transform (WT)-based method. Signal–noise ratio (SNR) and mean square error (MSE) between estimated signal and original signal are used as indicators to evaluate the performance. The results show that by using the present method, the SNR is higher while the MSE between estimated signal and original signal is smaller.