本文選題：心電信號(hào) + 質(zhì)量評(píng)估�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：心血管疾病是對(duì)人類(lèi)健康造成威脅的最嚴(yán)重的疾病之一,且具有高發(fā)病率、高患病率、高致殘率、高死亡率等特點(diǎn)。心電圖作為檢測(cè)和診斷心血管疾病的重要方法之一,在采集過(guò)程中,經(jīng)常受到各種噪聲的干擾。被噪聲干擾的心電信號(hào),不但增加了醫(yī)師的工作量,降低了診斷效率,而且增加了心臟監(jiān)視器的錯(cuò)誤報(bào)警率。為了減少噪聲帶來(lái)的影響,對(duì)心電信號(hào)的質(zhì)量進(jìn)行評(píng)估和信號(hào)去噪具有重要的意義。本文主要研究心電信號(hào)的質(zhì)量評(píng)估方法和去噪方法,根據(jù)心電采集設(shè)備和使用對(duì)象的不同,分為十二導(dǎo)聯(lián)心電信號(hào)和單導(dǎo)聯(lián)心電信號(hào)。在對(duì)十二導(dǎo)聯(lián)心電信號(hào)質(zhì)量評(píng)估中,本文提出了一種基于多特征融合的心電信號(hào)質(zhì)量評(píng)估方法,在該方法中提出了兩種特征融合方式:一種是基于規(guī)則的方法,另一種是基于統(tǒng)計(jì)特性和機(jī)器學(xué)習(xí)的方法。并用兩種方法在Physio Net/Computing in Cardiology Challenge 2011提供的數(shù)據(jù)庫(kù)中的訓(xùn)練集和測(cè)試集上進(jìn)行了測(cè)試,第一種方法獲得的分類(lèi)準(zhǔn)確率是92.8%和90.4%,時(shí)間性能是0.78秒,第二種方法獲得的分類(lèi)準(zhǔn)確率是94.0%和91.6%,時(shí)間性能是2.03秒。在對(duì)單導(dǎo)聯(lián)心電信號(hào)質(zhì)量評(píng)估中,本文從MIT-BIT心律不齊數(shù)據(jù)庫(kù)中選取了干凈的心電信號(hào),向其中加入了三種來(lái)自MIT-BIH噪聲壓力測(cè)試數(shù)據(jù)庫(kù)中的真實(shí)噪聲,根據(jù)噪聲水平的不同,制作了具有5個(gè)質(zhì)量等級(jí)的單導(dǎo)聯(lián)心電信號(hào)數(shù)據(jù)集。本文從單導(dǎo)聯(lián)心電信號(hào)中總共提取了10個(gè)信號(hào)質(zhì)量指數(shù),并用支持向量機(jī)分類(lèi)器在數(shù)據(jù)集上進(jìn)行訓(xùn)練和測(cè)試,經(jīng)過(guò)交叉驗(yàn)證,獲得的分類(lèi)準(zhǔn)確率是79.94%,單類(lèi)重疊準(zhǔn)確率是98.75%。根據(jù)單導(dǎo)聯(lián)心電信號(hào)中噪聲的類(lèi)型不同,本文針對(duì)每種類(lèi)型的噪聲使用了不同的去噪方法,包括數(shù)字濾波、自適應(yīng)濾波和小波濾波等方法,并用每種方法對(duì)不同質(zhì)量等級(jí)的心電信號(hào)進(jìn)行了去噪處理,然后從視覺(jué)效果和信噪比兩個(gè)方面進(jìn)行評(píng)價(jià)和比較,最后為不同噪聲類(lèi)型和不同質(zhì)量等級(jí)的心電信號(hào)選擇了適當(dāng)?shù)娜ピ敕椒ā；谇懊娴睦碚撗芯亢头治?本文設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)測(cè)試及應(yīng)用平臺(tái),對(duì)平臺(tái)的各個(gè)功能進(jìn)行了測(cè)試。測(cè)試結(jié)果驗(yàn)證了平臺(tái)各個(gè)功能的正確性和有效性,并對(duì)本文提出的質(zhì)量評(píng)估方法和采用的去噪方法提供了較好的支持。
[Abstract]:Cardiovascular disease is one of the most serious diseases that threaten human health, and has the characteristics of high incidence, high morbidity, high rate of disability, high mortality, etc. electrocardiogram is one of the most important methods to detect and diagnose cardiovascular diseases. In the process of collecting, it is often disturbed by various noises. But it increases the workload of doctors, reduces the efficiency of diagnosis, and increases the false alarm rate of the heart monitor. In order to reduce the influence of noise, it is of great significance to evaluate the quality of the ECG signal and to denoise the signal. This paper mainly studies the quality evaluation method and denoising method of ECG signal, and set up the ECG acquisition according to the ECG acquisition. Different objects are divided into twelve lead ECG signals and single lead ECG signals. In the quality evaluation of twelve lead ECG signals, this paper presents a quality evaluation method based on multi feature fusion. In this method, two feature fusion methods are proposed: one is a rule based method, the other is the other. Based on statistical characteristics and machine learning methods, and using two methods to test the training set and test set in the database provided by Physio Net/Computing in Cardiology Challenge 2011, the classification accuracy of the first method is 92.8% and 90.4%, the time performance is 0.78 seconds, and the classification accuracy of the second methods is 9. 4% and 91.6%, the time performance is 2.03 seconds. In the quality evaluation of single lead ECG signal, this paper selects clean ECG signals from the MIT-BIT arrhythmia database, and adds three kinds of real noise from the MIT-BIH noise pressure test database. According to the difference of noise level, there are 5 quality grades. In this paper, a total of 10 signal quality indexes are extracted from the single lead ECG signal, and the support vector machine classifier is trained and tested on the data set. After cross validation, the accuracy of the classification is 79.94%, and the single class overlap accuracy is 98.75%. based on the type of noise in the single lead ECG signal. In the same way, this paper uses different denoising methods for each type of noise, including digital filtering, adaptive filtering and wavelet filtering, and uses each method to denoise the ECG signals of different quality grades, and then evaluates and compares the two sides of the visual effect and the signal to noise ratio, and finally the different noise types. Based on the previous theoretical research and analysis, a test and application platform is designed and implemented, and the functions of the platform are tested. The test results verify the correctness and effectiveness of the various functions of the platform, and the quality evaluation method proposed in this paper. And the method of denoising is well supported.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R540.4;TN911.7
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本文編號(hào)：2015246