关键词: 压缩感知, 起搏心电信号, 重构算法, 岭回归

Abstract: Conventional reconstruction methodologies of compressed sensing for pacing ECG is suffered significantly from various noise through telemonitoring pacemaker by wireless communication networks. A novel method with the ridge regression regularization parameter K in the iteration of residual error is proposed to reduce the impact of noise to the recovery results. By ridge regression, it proves that superior K is negatively related to SNR, which provides a theoretical evidence to choose the suitable K and obtains the most accurate recovery signal. Comparative analysis is also introduced among the recovery algorithm based on the ridge regression, the Block Sparse Bayesian Learning and the orthogonal matching pursuit. The results show that the ridge regression method can not only maintain high recovery efficiency under the low SNR environment and increase the recovery accuracy of pacing ECG simultaneously.

Key words: compressed sensing, pacing ECG, recovery algorithm, ridge regression