ECG Signal Denoising and Reconstruction Based on Basis Pursuit

Changfang Chen,Ruixia Liu,Minglei Shu

doi:10.3390/app11041591

Abstract

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

Highlights

Noise removal is a fundamental problem in signal processing
It can be seen from the figure that after the algorithm filtering in this paper, the ECG signal that initially deviated from the baseline level returns to the baseline level, and the Gaussian white noise is effectively removed, which proves that the basis pursuit (BP)-alternating direction method of multipliers (ADMM) algorithm can effec
It can be seen from the figure that after the algorithm filtering in this paper, the ECG signal that initially deviated from the baseline level returns to the baseline level, and the Gaussian white noise is effectively removed, which proves that the BP algorithm and ADMM optimization (BP-ADMM) algorithm can effectively remove baseline wander noise and Gaussian white noise

Summary

Introduction

Noise removal is a fundamental problem in signal processing. Electrocardiography (ECG) is a vital biomedical tool for heart disease diagnosis. ECG is a fairly weak electric signal, and its amplitude is usually in the millivolt level. ECG noises include interference by the instrument itself, baseline wander, human activities, and other factors in the signal. Baseline wander is the most common noise in ECG signals, which has greatest interference to its amplitude. It usually causes the signal to deviate from the normal baseline level, affecting the ST segment and small waves such as P wave and T wave, etc. The changes of these morphological waves can seriously interfere with disease diagnosis [4,5,6]. Compared with ECG signals, baseline wander is a low-frequency noise, with a frequency of 0.05~2 Hz [7]

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Conclusion