本文關(guān)鍵詞：基于HHT和數(shù)據(jù)挖掘技術(shù)的白細(xì)胞信號(hào)識(shí)別研究　出處：《南昌大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 血細(xì)胞信號(hào)分類 希爾伯特-黃變換 集合經(jīng)驗(yàn)?zāi)B(tài)分解 特征提取 C4.5決策樹

【摘要】：血細(xì)胞信號(hào)的獲取作為血液分析儀里面的核心技術(shù),目前已經(jīng)得到了越來越廣泛的研究。血細(xì)胞信號(hào)具有脈沖形狀多樣、非線性和非平穩(wěn)的特點(diǎn),傳統(tǒng)方法需要將幅值不變的簡(jiǎn)諧信號(hào)定義為基底,給信號(hào)分析帶來了諸多限制,導(dǎo)致難以發(fā)現(xiàn)血細(xì)胞信號(hào)內(nèi)蘊(yùn)的很多生理或病理特征。因此,研究一種能有效挖掘血細(xì)胞信號(hào)內(nèi)在特征的分析方法具有重要研究意義。本文首先對(duì)血細(xì)胞技術(shù)的國(guó)內(nèi)外發(fā)展現(xiàn)狀及趨勢(shì)作了介紹,并且重點(diǎn)分析了血細(xì)胞信號(hào)識(shí)別過程中所面臨的問題。然后,針對(duì)血細(xì)胞信號(hào)非線性、非平穩(wěn)的特點(diǎn),設(shè)計(jì)了一種基于希爾伯特-黃變換(Hilbert-Huang Transform,HHT)和數(shù)據(jù)挖掘技術(shù)的分類識(shí)別方法。闡述了HHT的基本原理和數(shù)據(jù)挖掘理論。介紹了瞬時(shí)頻率的概念、HHT的本征模態(tài)函數(shù)(IMF)和經(jīng)驗(yàn)?zāi)B(tài)分解兩個(gè)關(guān)鍵步驟,以及對(duì)Hilbert譜和Hilbert邊際譜的定義。同時(shí)也說明了數(shù)據(jù)挖掘的具體過程和一些常用的數(shù)據(jù)挖掘技術(shù)。針對(duì)同一IMF分量里面會(huì)出現(xiàn)不同時(shí)間尺度成分這一現(xiàn)象,本文采用集合經(jīng)驗(yàn)?zāi)B(tài)分解方法(EEMD)自適應(yīng)地分解血細(xì)胞信號(hào),獲取信號(hào)的Hilbert譜、Hilbert邊際譜和HHT三維時(shí)頻譜圖。通過對(duì)這些譜圖數(shù)據(jù)的分析來提取特征向量：給出所要提取的特征的定義和公式,計(jì)算IMF能量與信號(hào)總能量的比值、中心頻率與強(qiáng)度,并選出其中具有較好區(qū)分度的特征值。把這些有效特征值作為分類和預(yù)測(cè)算法-C4.5決策樹的輸入信息,結(jié)合剪枝技術(shù)得到最終的分類模式,并與基于時(shí)間域的血細(xì)胞信號(hào)分類識(shí)別進(jìn)行對(duì)比。最后,采用準(zhǔn)確率和精度、lift圖、ROC曲線、魯棒性和可解釋性等一系列標(biāo)準(zhǔn)對(duì)模式進(jìn)行評(píng)估和比對(duì),找出其中的最佳分類模型。研究表明,基于HHT提取特征來構(gòu)建的分類模型與傳統(tǒng)的基于時(shí)間域的分類模型相比,區(qū)分度明顯提高。所以,通過對(duì)血細(xì)胞信號(hào)固有特性的分析,采用HHT算法,解決了其非線性非平穩(wěn)特點(diǎn)帶來的諸多難題,有利于提取和發(fā)現(xiàn)血細(xì)胞信號(hào)的生理或病理特征,給臨床醫(yī)學(xué)診斷提供了一種新的思路。
[Abstract]:Blood cell signal acquisition as the core technology of blood analyzer inside, has been extensively studied. Blood cell signal with pulse shapes, nonlinear and non-stationary characteristics, the traditional method of harmonic signal amplitude is constant needs to be defined as substrate, have brought many restrictions in signal analysis, may be difficult to find the intrinsic blood cell signal many physiological or pathological features. Therefore, it has important significance to study an intrinsic characteristic of effective mining blood cell signal analysis method. Firstly, the blood cell technology at home and abroad the status quo and development trend were introduced, and analyzed the face blood cell signal recognition process. Then, the blood cell signal nonlinear, nonstationary characteristics, a design based on Hilbert Huang transform (Hilbert-Huang Transform, HHT) and data mining technology Classification and recognition method of operation. Elaborates the basic theory and principle of HHT data mining. This paper introduces the concept of instantaneous frequency, the intrinsic mode function HHT (IMF) and EMD two key steps, and the definition of the Hilbert spectrum and Hilbert marginal spectrum. It also states the process of data mining and some commonly used data mining technology. For the same IMF components which appear in different time scale components of this phenomenon, this paper adopts the method of ensemble empirical mode decomposition (EEMD) adaptive decomposition of blood cell signal acquisition, signal Hilbert spectrum, Hilbert marginal spectrum and HHT three-dimensional time-frequency spectrum. Based on the analysis of these spectra data to feature extraction: the definition and characteristics of the formula to be extracted by the calculation of the ratio of IMF energy and the total signal energy, center frequency and intensity, and the selected feature which has better discrimination value. These characteristic values as input information classification and prediction algorithm of -C4.5 decision tree based classification model, pruning technique is final, and compared with the classification and recognition of blood cell signal based on time domain. Finally, the accuracy and precision, lift diagram, ROC curve, robustness and a series of standard interpretation etc. on the mode of assessment and comparison, find out the best classification model of them. Research shows that the classification model of HHT to construct the feature extraction and the traditional classification model based on time domain comparison based on discrimination is obviously improved. So, through the analysis on the inherent characteristics of blood cell signal, using HHT algorithm to solve the nonlinear and non many the characteristics of stable problem, is conducive to the extraction and found blood cell signal physiological or pathological features, provides a new way for clinical diagnosis.

【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：R446.11;TP311.13

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本文編號(hào)：1396745