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

TY - JOUR

T1 - Priori-block sparse Bayesian learning algorithm for compressed sensing based ECG recovery

AU - Luo, Kan

AU - Li, Jianqing

AU - Wang, Zhiqang

AU - Cai, Zhipeng

PY - 2014/8

Y1 - 2014/8

N2 - Compressed sensing (CS) has advantages in low-cost, low-power and long term wireless electorcardiogram(ECG) applications. However, there are some problems in existing reconstruction algorithms, which include the unsatisfied signal quality, huge computation 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.

AB - Compressed sensing (CS) has advantages in low-cost, low-power and long term wireless electorcardiogram(ECG) applications. However, there are some problems in existing reconstruction algorithms, which include the unsatisfied signal quality, huge computation 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.

UR - https://www.scopus.com/pages/publications/84907249349

M3 - Article

AN - SCOPUS:84907249349

SN - 0254-3087

VL - 35

SP - 1883

EP - 1889

JO - Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument

JF - Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument

IS - 8

ER -