本文關鍵詞：遠程心電張量特征抽取與分析　出處：《上海交通大學》2015年博士論文　論文類型：學位論文

  更多相關文章： 張量特征抽取 特征降維 張量學習方法 心電分析 輔助診斷 稀疏編碼 核方法

【摘要】：隨著物聯(lián)網(wǎng)技術,智能移動設備的不斷普及,大規(guī)模遠程心電的診斷平臺不斷成熟并且被廣泛使用。由于遠程心電診斷平臺的規(guī)模巨大,自動的輔助診斷就顯得尤為的重要,尤其是危重和急癥病例的識別。本文的重點就是從去噪,預處理,特征抽取,特征降維,分類的整個流程中所涉及的基礎理論和關鍵技術方面的問題。本文主要從輔助心電分析的角度出發(fā),研究如何結合心電的特點,解決心電去噪、預處理、特征抽取、特征降維、特征分類和模式識別等問題,并且結合心電的不同特點提出相應的實現(xiàn)算法。并最終將這些方法應用到心電的輔助診斷和分類問題上。本文的主要工作和創(chuàng)新點包括以下幾個方面:1.對于心電預處理過程來說,去噪是至關重要的環(huán)節(jié)。傳統(tǒng)的心電去噪方法往往帶來心電波形的走樣和關鍵特征的丟失。另外,心電12導聯(lián)信號本身存在著很大的冗余。這里我們提出了一種新的方法充分利用了心電信號本身的冗余性特點。我們重構出心電的2d和3d心電向量信號,然后通過投影重新獲取多組原始心電信號,我們充分利用了波形中重要波形所在位置的信息,利用基于先驗知識的加權主成分分析方法來去除噪聲并且從重建心電信號中抽取出有用波形。2.從病理學角度出發(fā),使用人工診斷過程中使用的特征進行分析有很大的困難。不同的心電疾病有著不同的心電波形,而且對于心電來說,有著很多種類的疾病,所以要從心電中準確無誤地抽取出心電的病理學特征非常困難。因此我們嘗試從機器學習的角度出發(fā),從數(shù)據(jù)中學習出機器可以理解和容易處理的特征來進行分析,最終得到好的分析效果。除此之外,心電信號在頻率上存在著一些對分類非常有用的特征,所以我們嘗試在時頻空的復合域上進行分析。所以我們提出了一些基于張量和多線性分析的方法直接在張量空間對數(shù)據(jù)進行分析,來嘗試克服張量空間里特征稀疏和其它相關問題。3.張量算法的通病是目標函數(shù)非凸,容易落入局部最小解等問題。為了能夠以大概率得到全局最優(yōu)解。這里我們提出了一種計算框架,對于帶約束的和不帶約束的張量問題來進行求解。使用我們的方法,和其它張量學習算法所遇到的收斂困難等問題可以不同程度得到改善,而且往往可以得到更優(yōu)的解。4.對于張量特征抽取算法,分為基于T2V映射并且以向量為輸出的還有基于T2T映射并且以張量為輸出的。本文提出的張量特征抽取算法基本上都是基于(T2V)的。雖然對于以張量為輸出的張量特征抽取算法,我們可以對它的張量輸出進行向量化然后使用現(xiàn)有向量空間分類器進行分類,但是這樣也會面臨結構信息丟失、參數(shù)過多過擬合、小樣本問題等等。所以我們提出了一些可以直接以張量為輸入的特征分類方法,最后和其它的方法進行比較。以上幾項工作都是基于心電的特點提出的去噪、特征抽取和分類算法,本研究在給出模型框架的同時還給出了具體實現(xiàn)算法,并針對各種應用問題進行了實驗分析。
[Abstract]:With the networking technology, the popularity of smart mobile devices, large-scale remote ECG diagnosis platform continues to mature and has been widely used. Because the remote ECG diagnosis platform of large scale, automatic diagnosis is particularly important, especially the identification of critical cases. This paper focuses on denoising preprocessing, feature extraction, feature reduction, basic theory and the key technical problems involved in the whole process of classification. This paper mainly from the auxiliary ECG analysis point of view, study how to combine the characteristics of ECG, solve the ECG denoising, preprocessing, feature extraction, feature reduction, feature classification and pattern recognition etc. and, according to the different characteristics of the proposed ECG algorithms. And finally apply these methods to diagnosis and ECG classification problems. The main work of this paper and the innovation package Including the following aspects: 1. for ECG preprocessing, denoising is very important. The loss of the traditional ECG denoising method often leads ECG waveform aliasing and key characteristics. In addition, 12 lead ECG signal itself has great redundancy. Here we proposed a new method to make full use of the the redundancy characteristic of ECG signal itself. We reconstructed the 2D ECG and VCG 3D signal, and then through the projection to re acquire multiple sets of the ECG signal, we make full use of the important position of the waveform waveform information, using weighted principal component analysis method based on prior knowledge to remove noise and extract useful waveform.2. starting from the perspective of pathology from reconstruction of ECG signals, the characteristics of using artificial diagnosis in the process of analysis is very difficult. The ECG of different diseases ECG waveform, and the ECG, there are many kinds of diseases, so from ECG accurate to extract ECG pathological features very difficult. So we try from the view of machine learning, data from the learning machine can understand and easy processing characteristics to analyze, finally get the analysis good effect. In addition, there are some features of ECG signal classification is very useful in frequency, so we try to analyze the time-frequency domain composite empty. So we put forward some analysis methods based on multilinear tensor and tensor space directly in the analysis of the data, to try to overcome the defects of tensor in the space of sparse features and other related problems of.3. tensor algorithm is non convex objective function, easy to fall into the local minimum problem. In order to get the probability of global optimal solution. Here we propose a computational framework for constrained and unconstrained tensor problem to solve the problem. We use the methods, problems encountered in the learning algorithm convergence difficulties and other tensor can improved to a certain extent, and can often get a better solution for the.4. tensor feature extraction algorithm, divided into based on the T2V mapping and vector output are based on T2T mapping and output to the tensor. This paper proposes a tensor feature extraction algorithm is basically based on (T2V). Although for the output of the Zhang Liangte tensor feature extraction algorithm, we can output the tensor vector and then use the existing vector space the classifier, but it will also face the loss of information structure, parameter over fitting, the small sample problem and so on. So we can put forward some directly to the tensor Feature classification method of input, and finally compared with other methods. The above work is presented the characteristics of ECG based denoising, feature extraction and classification algorithm, this study is also given in the model framework and gives the realization algorithm, and the experimental analysis for various applications.

【學位授予單位】：上海交通大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：R540.41

【參考文獻】

相關期刊論文 前1條

1 季虎;孫即祥;毛玲;;基于小波變換與形態(tài)學運算的ECG自適應濾波算法[J];信號處理;2006年03期

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本文編號：1439137