本文選題：起搏心電信號 + 壓縮感知　； 參考：《重慶理工大學(xué)》2017年碩士論文

【摘要】：傳統(tǒng)起搏器監(jiān)測方法受制于信號頻率、功耗、監(jiān)測方式等因素,因而在進(jìn)行實時監(jiān)測時難以應(yīng)對大量起搏心電數(shù)據(jù)的采樣、處理與傳輸。壓縮感知理論可以進(jìn)行信號的低功耗采樣,實現(xiàn)監(jiān)測終端的低功耗、高效率信號獲取,并在監(jiān)測終端利用相應(yīng)優(yōu)化算法恢復(fù)出逼近原始信號的重構(gòu)信號。本研究致力于探索一種適用于起搏心電信號的信號感知以及重構(gòu)方法,為以后利用壓縮感知技術(shù)實現(xiàn)起搏心電信號遠(yuǎn)程監(jiān)測系統(tǒng)的設(shè)計提供技術(shù)支撐。本課題主要結(jié)合了壓縮感知理論并針對起搏心電信號自身特性,對其中的信號采樣、信號重構(gòu)等技術(shù)進(jìn)行理論及實驗研究。主要從起搏心電的壓縮感知過程及重構(gòu)算法著手,重點針對噪聲環(huán)境下真實信號的不可知性,將有偏重構(gòu)的思想引入到重構(gòu)算法中,并通過實驗測試以及理論證明,對提出的算法進(jìn)行多角度評測,驗證了算法的有效性。主要的研究工作有:第一,根據(jù)起搏心電信號特點,設(shè)計隨機稀疏的二值測量矩陣,并應(yīng)用于后續(xù)的信號感知以及重構(gòu)過程。第二,基于分塊稀疏貝葉斯學(xué)習(xí),對起搏心電信號的壓縮感知進(jìn)行研究,探索該算法框架下的信號重構(gòu)。第三,針對噪聲環(huán)境下的信號重構(gòu)問題,根據(jù)現(xiàn)有壓縮感知重構(gòu)算法的特點,結(jié)合嶺回歸有偏重構(gòu)思想改進(jìn)出適用于起搏心電信號的重構(gòu)算法,在重構(gòu)精度及重構(gòu)效率兩個方面相較于原有算法均有提升;并對嶺參數(shù)的優(yōu)化方法進(jìn)行研究以確定出噪聲環(huán)境下起搏心電信號重構(gòu)的最優(yōu)嶺參數(shù)值。第四,對研究內(nèi)容中測量矩陣的設(shè)計效果及重構(gòu)算法的改進(jìn)成效進(jìn)行評價:對測量矩陣從感知效率、矩陣構(gòu)造等角度進(jìn)行對比;對重構(gòu)算法從重構(gòu)精度及重構(gòu)效率以及算法對信號、噪聲類型的普適性等方面分別對比。
[Abstract]:Traditional pacemaker monitoring methods are limited by signal frequency, power consumption, monitoring mode and other factors, so it is difficult to deal with the sampling, processing and transmission of a large number of pacemaker data in real-time monitoring. The compression sensing theory can sample the signal with low power consumption and achieve the low power consumption and high efficiency signal acquisition. The reconstruction signal approaching the original signal can be recovered by the corresponding optimization algorithm in the monitoring terminal. This study is devoted to exploring a method of signal sensing and reconstruction suitable for pacing ECG signals, which provides technical support for the design of remote monitoring system for pacing ECG signals by using compression sensing technology in the future. This paper mainly combines the theory of compression sensing and the characteristics of pacemaker ECG signals, and carries out theoretical and experimental research on the signal sampling and signal reconstruction techniques. This paper mainly starts with the compression process and reconstruction algorithm of pacing electrocardiogram, focusing on the unknowability of real signal in noise environment. The idea of biased reconstruction is introduced into the reconstruction algorithm, and it is proved by experiment and theory. The effectiveness of the proposed algorithm is verified by multi-angle evaluation. The main research works are as follows: first, according to the characteristics of pacing ECG, a random sparse binary measurement matrix is designed and applied to the subsequent signal sensing and reconstruction process. Secondly, based on block sparse Bayesian learning, the compression perception of pacemaker ECG signal is studied, and the signal reconstruction under the framework of this algorithm is explored. Thirdly, aiming at the problem of signal reconstruction in noisy environment, according to the characteristics of existing compression sensing reconstruction algorithms and combining with the biased conception of ridge regression, the reconstruction algorithm suitable for pacing ECG signals is improved. Compared with the original algorithm, the reconstruction accuracy and efficiency are improved, and the optimization method of ridge parameters is studied to determine the optimal ridge parameters of ECG reconstruction in noisy environment. Fourthly, the design effect of the measurement matrix and the improvement of the reconstruction algorithm are evaluated. The measurement matrix is compared from the aspects of perception efficiency and matrix construction. The reconstruction algorithm is compared in terms of reconstruction precision, reconstruction efficiency and the universality of the signal and noise types.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R540.4;TN911.7

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本文編號：1875992