本文選題：壓縮感知 + 起搏心電信號(hào)��； 參考：《計(jì)算機(jī)工程與應(yīng)用》2017年18期

【摘要】：針對(duì)傳統(tǒng)壓縮感知重構(gòu)算法在起搏心電信號(hào)遠(yuǎn)程監(jiān)測(cè)過(guò)程中易受噪聲干擾的問(wèn)題,提出在利用正交匹配追蹤進(jìn)行殘差更新的迭代過(guò)程中引入嶺回歸正則化參數(shù)K,降低噪聲對(duì)重構(gòu)結(jié)果的影響。利用嶺跡法證明了最佳K值與信噪比呈負(fù)相關(guān),為選取K值以獲得更接近真實(shí)解的重構(gòu)信號(hào)提供了理論依據(jù)。對(duì)基于嶺回歸的重構(gòu)算法與分塊稀疏貝葉斯學(xué)習(xí)算法、正交匹配追蹤算法進(jìn)行了對(duì)比分析,實(shí)驗(yàn)結(jié)果表明,在低信噪比環(huán)境下,引入了嶺回歸思想的算法在保留高重構(gòu)效率的同時(shí)提高了重構(gòu)精度。
[Abstract]:In view of the problem that the traditional compression sensing reconstruction algorithm is prone to noise interference in the remote monitoring of the pacemaker signal, the ridge regression regularization parameter K is introduced in the iterative process of residual updating using orthogonal matching tracking to reduce the effect of noise on the reconstruction results. The ridge trace method is used to prove that the best K value and the signal to noise ratio are negative phase In order to select the K value to obtain the refactoring signal which is closer to the real solution, the reconstruction algorithm based on ridge regression and the block sparse Bayesian learning algorithm and the orthogonal matching tracking algorithm are compared. The experimental results show that the algorithm of ridge regression thinking is reserved for high reconstruction efficiency under the environment of low signal to noise ratio. At the same time, the precision of reconstruction is improved.
【作者單位】： 重慶理工大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(No.61502064) 重慶市自然科學(xué)基金(No.cstc2011jj A40002) 重慶市教委科學(xué)技術(shù)研究項(xiàng)目(A類)(No.KJ110813)
【分類號(hào)】：R540.4;TN911.7
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本文編號(hào)：2043026