本文選題：心肌梗死　切入點(diǎn)：心電信號(hào)　出處：《鄭州大學(xué)》2017年碩士論文

【摘要】：隨著人們生活水平和生活方式的變化,心血管病的發(fā)病率和死亡率逐年升高,同時(shí)伴隨有年輕化的趨勢(shì)。心肌梗死作為心血管疾病的最大死因之一,是嚴(yán)重影響人們生命健康的重大疾病。目前對(duì)心肌梗死的診斷主要有心肌標(biāo)記物水平和心電圖分析兩種方式,而心肌標(biāo)記物的提取和分析復(fù)雜多樣,很難在短時(shí)間內(nèi)完成,因此對(duì)心電圖的自動(dòng)分析成為目前專家學(xué)者研究的重點(diǎn)。本文基于心肌梗死數(shù)據(jù)的背景下,總結(jié)前人的研究經(jīng)驗(yàn),對(duì)心電信號(hào)的噪聲預(yù)處理、心電特征波形檢測(cè)以及心肌梗死輔助診斷模型的建立進(jìn)行了詳細(xì)的研究。(1)心電信號(hào)的噪聲預(yù)處理:本文按照心電信號(hào)各種噪聲的來(lái)源以及實(shí)時(shí)性分析的要求,設(shè)計(jì)了一種能一次性濾除三種干擾的濾波器組對(duì)信號(hào)進(jìn)行濾波。這種方法不僅可以有效濾除信號(hào)的低頻基線漂移、工頻干擾及肌電干擾等不同頻率的噪聲,而且不影響原始信號(hào)的重要信息。(2)心電特征波形的檢測(cè):本文提出了一種改進(jìn)小波算法檢測(cè)心電特征波形,這種算法在連續(xù)小波多尺度變換的基礎(chǔ)上通過(guò)自適應(yīng)地改變閾值和增加窗口函數(shù)檢測(cè)特征波形,極大的提高了特征提取的準(zhǔn)確性。小波基函數(shù)的選取在很大程度上有效的抑制了在心電信號(hào)噪聲預(yù)處理階段殘留的噪聲干擾,而小波尺度的選擇和自適應(yīng)閾值的選取則是經(jīng)過(guò)大量的實(shí)驗(yàn)分析決定的。用PTB診斷數(shù)據(jù)庫(kù)中的部分?jǐn)?shù)據(jù)對(duì)此算法進(jìn)行驗(yàn)證,仿真結(jié)果表明,R波檢測(cè)的準(zhǔn)確率達(dá)到99%以上,能準(zhǔn)確定位干擾嚴(yán)重和波形畸變較大的心電特征信號(hào)。(3)心肌梗死輔助診斷模型的建立:基于心電信號(hào)的各典型波幅值、間期以及電位偏移情況等特征參數(shù),主要設(shè)計(jì)了Logistic回歸、BP神經(jīng)網(wǎng)絡(luò)以及基于K-CV的SVM三種自動(dòng)分類診斷模型。在建模分析的過(guò)程中,重點(diǎn)對(duì)SVM的參數(shù)c和g的最優(yōu)值進(jìn)行了研究,提出了一種8折K-CV的二次尋優(yōu)方法,很大程度上提高了分類器的性能。最后對(duì)這三種模型的性能進(jìn)行了驗(yàn)證,結(jié)果表明,基于K-CV的SVM模型準(zhǔn)確率達(dá)到了99.19%,可以為心肌梗死疾病的輔助診斷提供重要的理論指導(dǎo)和臨床意義。
[Abstract]:With the change of people's living standard and lifestyle, the morbidity and mortality of cardiovascular disease increase year by year, and accompanied by the trend of younger age. Myocardial infarction is one of the biggest causes of death of cardiovascular disease. At present, the diagnosis of myocardial infarction mainly includes two ways: myocardial marker level and electrocardiogram analysis. However, the extraction and analysis of myocardial markers are complex and diverse, so it is difficult to complete the diagnosis in a short time. Therefore, the automatic analysis of ECG has become the focus of experts and scholars. Based on the background of myocardial infarction data, this paper summarizes the previous research experience, the ECG signal noise preprocessing, The detection of ECG characteristic waveform and the establishment of auxiliary diagnosis model of myocardial infarction were studied in detail. The noise pretreatment of ECG signal was carried out. According to the source of various kinds of noise of ECG signal and the requirement of real-time analysis, A filter bank which can filter three kinds of interference at one time is designed to filter the signal. This method can not only effectively filter the noise of low frequency baseline drift, power frequency interference and myoelectric interference, etc. And does not affect the important information of the original signal. 2) ECG characteristic waveform detection: this paper proposes an improved wavelet algorithm to detect ECG characteristic waveform, On the basis of continuous wavelet multiscale transform, this algorithm adaptively changes the threshold value and increases the window function to detect the characteristic waveform. The accuracy of feature extraction is greatly improved. The selection of wavelet basis function can effectively suppress the residual noise interference in ECG signal preprocessing. The selection of wavelet scale and adaptive threshold are determined by a large number of experiments. The algorithm is verified by some data in PTB diagnostic database. The simulation results show that the accuracy of R wave detection is over 99%. The establishment of an auxiliary diagnostic model of myocardial infarction, which can accurately locate the ECG characteristic signal with serious interference and large waveform distortion: based on the characteristic parameters of ECG amplitude, interval and potential deviation, etc. This paper mainly designs Logistic regression BP neural network and SVM automatic classification and diagnosis model based on K-CV. In the process of modeling and analysis, the optimal values of parameters c and g of SVM are studied, and a quadratic optimization method of 80% K-CV is put forward. The performance of the classifier is improved to a great extent. Finally, the performance of the three models is verified, and the results show that, The accuracy of SVM model based on K-CV is 99.19, which can provide important theoretical guidance and clinical significance for the auxiliary diagnosis of myocardial infarction disease.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R542.22;TN911.7

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