本文選題：盲源分離　切入點：非負(fù)矩陣分解　出處：《太原理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：心電信號(Electrocardiogram,ECG)是人體的重要生理信號之一,其中包含著大量關(guān)于心臟的病變、病理狀況的相關(guān)信息,也反映了心臟與心血管的結(jié)構(gòu)及其生理和病理的特性。心電信號的分析診斷對心血管等系統(tǒng)疾病的診斷具有很重要的意義,其精確度和可靠度會直接地影響心臟疾病患者的臨床醫(yī)療診斷和愈后的效果評價。然而,傳統(tǒng)的心電識別方法是醫(yī)生臨床聽診,很顯然此過程具有一定的主觀判斷和不穩(wěn)定性,準(zhǔn)確性比較差。現(xiàn)階段,從人體體表獲取的心電信號,或多或少都會受到工頻干擾、肌電干擾以及基線漂移等多種噪聲的干擾。因此消除心電信號的噪聲,對之后臨床研究的特征波檢測以及病理診斷等需求都具有重要的意義。 非負(fù)矩陣分解(Non-negative Matrix Factorization, NMF),作為一種新興的特征分離方法,由Lee和Seung等人在盲源分離的應(yīng)用背景下于1999年提出,并發(fā)表在Nature雜志上,并且慢慢發(fā)展成為了信號處理和數(shù)據(jù)分析的有效方法。通過在矩陣分解過程中加入非負(fù)的矩陣元素,非負(fù)矩陣分解使得分解結(jié)果呈現(xiàn)出的完全不同,完成了降維的非線性目標(biāo)。隨著盲源信號處理研究的逐漸加深,非負(fù)矩陣分解已經(jīng)逐步成為信號處理、生物醫(yī)學(xué)和圖像處理等多個研究領(lǐng)域中最受學(xué)者青睞的數(shù)據(jù)處理工具之一。本文將非負(fù)矩陣分解應(yīng)用于對心電信號的消噪,具有收斂速度快、稀疏性、非負(fù)性、降維等特性。 在對基本NMF算法的學(xué)習(xí)中,NMF加人了非負(fù)的約束。這樣,通過分解得到的基信號數(shù)據(jù)以及用于重構(gòu)的權(quán)重系數(shù)都是非負(fù)的。在這種模式下,只允許線性疊加運(yùn)算,這就保證了“局部構(gòu)成整體”模式。因此,NMF被認(rèn)為是提取局部特征的一種方法。但是,NMF算法得到的“部分”有時候并不是像我們預(yù)期的那樣局部化,而且基本NMF方法在某些時候的識別率不是很高。 出于對NMF原算法的深入學(xué)習(xí),本人在研究局部信號數(shù)據(jù)時建立PNMF算法,其目的是通過引入稀疏性限制獲得編碼矢量(矩陣H)真正的局部分解對象,并使基本組件(矩陣W)局部稀疏化,加強(qiáng)基成分的局部化特征,使算法適用于局部特征非常重要的應(yīng)用。 本文結(jié)合NNF算法特點及心電信號特征,首次提出了一種新的NMF算法——PNMF對心電信號盲源分離。結(jié)合MIT/BIH國際標(biāo)準(zhǔn)數(shù)據(jù)庫中ECG數(shù)據(jù)和模擬基線漂移、工頻干擾以及肌電干擾噪聲合成含噪聲心電信號,并應(yīng)用新提出的PNMF算法進(jìn)行盲源分離實驗研究,對分離結(jié)果采用信噪比(Signal to Noise Ratio,SNR)評價參數(shù)進(jìn)行量化評價,與3種不同的NMF算法進(jìn)行了對比,同時將PNMF算法與FastICA算法的的分離結(jié)果做比較,從分離精度的角度來看,本文的算法取得了最佳的效果。實驗結(jié)果表明PNMF算法可有效分離心電源信號,為實際心電后期準(zhǔn)確診斷提供了一定的參考依據(jù)。
[Abstract]:Electrocardiogramme (ECG) is one of the important physiological signals in human body, which contains a lot of information about the pathological changes and pathological conditions of the heart. It also reflects the structure of the heart and the cardiovascular system and its physiological and pathological characteristics. The analysis and diagnosis of ECG signals are of great significance for the diagnosis of cardiovascular diseases and other systemic diseases. The accuracy and reliability of ECG can directly affect the clinical diagnosis and evaluation of the curative effect of patients with heart disease. However, the traditional ECG recognition method is clinical auscultation, so it is obvious that this process has some subjective judgment and instability. The accuracy is poor. At this stage, ECG signals obtained from the body surface of the human body are more or less interfered by various noises such as power frequency interference, myoelectric interference and baseline drift. Therefore, the noise of ECG signals is eliminated. It is of great significance for the requirement of characteristic wave detection and pathological diagnosis in later clinical research. Non-negative Matrix factorization, as a new feature separation method, was proposed by Lee and Seung in 1999 under the background of blind source separation and published in Nature magazine. By adding non-negative matrix elements into the matrix decomposition process, the non-negative matrix decomposition makes the decomposition result completely different. With the development of blind source signal processing, the nonnegative matrix decomposition has gradually become signal processing. One of the most popular data processing tools in biomedicine and image processing. In this paper, non-negative matrix decomposition is applied to de-noising ECG signals, which has the characteristics of fast convergence, sparsity, non-negativity and dimensionality reduction. In the learning of the basic NMF algorithm, the nonnegative constraints are added. Thus, the base signal data obtained by decomposition and the weight coefficients used for reconstruction are non-negative. In this mode, only linear superposition operations are allowed. So NMF is considered to be a way to extract local features. But sometimes the "part" of the NMF algorithm is not as localized as we might expect. Moreover, the recognition rate of the basic NMF method is not very high at some times. In order to study the original algorithm of NMF, I establish the PNMF algorithm when studying the local signal data. The purpose of this algorithm is to obtain the real local decomposition object of the encoding vector (matrix H) by introducing the sparsity restriction. It also makes the basic component (matrix W) local sparse, strengthens the localization feature of the base component, and makes the algorithm suitable for the application of local feature. Based on the characteristics of NNF algorithm and ECG signal, this paper presents a new NMF algorithm for blind source separation of ECG signals for the first time, combined with ECG data and analog baseline drift in MIT/BIH international standard database. Power frequency interference and myoelectric interference noise are used to synthesize noise-containing ECG signals, and the new PNMF algorithm is used to study the blind source separation experiment. The signal to noise ratio (SNR) signal to Noise (SNR) evaluation parameters are quantitatively evaluated. Compared with three different NMF algorithms, the separation results of PNMF algorithm and FastICA algorithm are compared from the point of view of separation accuracy. The experimental results show that the PNMF algorithm can effectively separate the signal of cardiac power supply and provide a certain reference for the accurate diagnosis in the later period of ECG.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 魏樂;基于非負(fù)矩陣分解算法進(jìn)行盲信號分離[J];電光與控制;2004年02期

2 趙菊敏;李燈熬;張海燕;郭t樻，

本文編號：1562054