心电压缩感知恢复先验块稀疏贝叶斯学习算法

Priori-block sparse Bayesian learning algorithm for compressed sensing based ECG recovery

压缩感知在低成本、低功耗、长时间的无线心电信号应用上具有优势。但现有重构算法中存在重构信号质量不理想、较大的计算量以及不能自适应噪声变化等问题。本文针对非稀疏心电信号快速精确压缩感知重构提出了先验块稀疏贝叶斯学习(P-BSBL)算法。算法在块稀疏贝叶斯学习基础上,根据心电信号先验引入了近似零解空间初值设置和数字特征迭代停止条件。为了验证算法效果,提出的方法在MIT-BIH心电数据库上进行了仿真实验。实验结果表明P-BSBL能够实现高效非稀疏心电信号高信号质量重构。P-BSBL在正常和非正常心电信号重构上都优于凸优化和贪婪方法;适用于高数据压缩比和噪声变化的心电信号重构。

Compressed sensing （CS）has advantages in low-cost,low-power and long term wireless electorcardiogram（ECG）applica-tions.However,there are some problems in existing reconstruction algorithms,which include the unsatisfied signal quality,huge compu-tation task and no-adaptive to noise.To accurately reconstruct the non-sparse ECG signal,a priori block sparse Bayesian learning （P-BSBL）algorithm is proposed in this paper.Based on the block sparse Bayesian learning,the P-BSBL introduces priori of ECG signals to enhance the performance of the algorithm,which adopts the “nearby”zero solution space as the initial values and the signal statistical characteristic as the stop condition.The numerical experiments on MIT-BIH ECG database were conducted to verify the algorithm.The results show that the proposed method can efficiently reconstruct the non-sparse ECG signal with high signal quality.The P-BSBL has better performance compared with the convex optimization and greed methods;and it is more suitable for the ECG signal reconstruction with high data compression ratio and variable signal-to-noise ratio.