本文選題：心電信號(hào)　切入點(diǎn)：ST段　出處：《鄭州大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：急性心肌梗死是危害中老年人身體健康的常見疾病,在其發(fā)病初期主要是通過心電圖情況來(lái)進(jìn)行檢測(cè),目前針對(duì)該病癥的有效診斷及防治已成為醫(yī)學(xué)界所面臨的一項(xiàng)新的課題。以往常規(guī)的心電信號(hào)采集系統(tǒng)中均采用12導(dǎo)聯(lián)進(jìn)行采集,但多數(shù)患者在疾病突發(fā)時(shí)往往不在醫(yī)院,會(huì)對(duì)診斷及救護(hù)帶來(lái)不便,因此,本研究在開發(fā)一種便攜式單導(dǎo)聯(lián)心電監(jiān)護(hù)手機(jī)的背景下,進(jìn)行了單導(dǎo)聯(lián)心電信號(hào)檢測(cè)技術(shù)算法研究。該技術(shù)可以讓患者隨時(shí)隨地的對(duì)心電圖進(jìn)行監(jiān)測(cè)和診斷,成為了心電監(jiān)護(hù)設(shè)備的一種發(fā)展方向。 急性心肌梗死早期主要體現(xiàn)為心電圖的ST段變化,因此心電信號(hào)ST段的正確識(shí)別對(duì)于急性心肌梗死的診斷具有重要的意義。ST段代表心室除極完成后復(fù)極過程的電位變化,極易受到心肌缺血、噪聲等外界干擾,此外ST段形態(tài)在同一個(gè)體的不同導(dǎo)聯(lián)情況下也存在差異,因此ST段的計(jì)算機(jī)自動(dòng)識(shí)別成熟度遠(yuǎn)低于QRS波識(shí)別技術(shù)。正確識(shí)別ST段起始點(diǎn)、終止點(diǎn)、ST段形態(tài)特征及其電平測(cè)量較困難,且目前尚無(wú)統(tǒng)一測(cè)量標(biāo)準(zhǔn)。針對(duì)于ST段形態(tài)特征和檢測(cè)難點(diǎn),本文提出了一種基于二維云模型理論的心電信號(hào)ST檢測(cè)方法。具體研究?jī)?nèi)容如下： (1)根據(jù)ST段所受的噪聲特點(diǎn),本文使用了零相位數(shù)字濾波方法,設(shè)計(jì)出一個(gè)9階Chebyshev帶通數(shù)字濾波器來(lái)對(duì)心電信號(hào)進(jìn)行濾波降噪,通過濾波前后的信號(hào)頻譜分析,驗(yàn)證了該濾波器在保持心電信號(hào)形態(tài)不失真(主要是ST段形態(tài))的條件下,能夠有效消除低頻基線漂移和工頻干擾。 (2)在心電信號(hào)特征參數(shù)的提取方面,對(duì)于心電信號(hào)QRS波,本文采用差分閾值法進(jìn)行檢測(cè),并綜合運(yùn)用時(shí)間移動(dòng)窗口、自適應(yīng)等技術(shù),提高了檢測(cè)精度,克服了小波變換計(jì)算量大、神經(jīng)網(wǎng)絡(luò)模板訓(xùn)練時(shí)間長(zhǎng)等缺點(diǎn),比較適合于便攜式單導(dǎo)聯(lián)心電監(jiān)護(hù)手機(jī)的開發(fā)；同時(shí)采用局域變換算法對(duì)ST段起始點(diǎn)、終止點(diǎn)進(jìn)行提取,仿真實(shí)驗(yàn)結(jié)果顯示算法具有較高的精度。 (3)針對(duì)心電數(shù)據(jù)模糊性和隨機(jī)性較大的特點(diǎn),本文提出了一種基于云模型的心電信號(hào)ST段的檢測(cè)方法,能夠通過待測(cè)信號(hào)對(duì)判別規(guī)則云隸屬度大小的判斷,來(lái)進(jìn)行心電信號(hào)ST段形態(tài)判定。首先采用云模型對(duì)ST段內(nèi)的大量采樣點(diǎn)特征(數(shù)據(jù)點(diǎn)的電位值、一階導(dǎo)數(shù)和二階導(dǎo)數(shù))所出現(xiàn)的頻率進(jìn)行聚類分析,獲得具有自身特性的ST段特征綜合云,進(jìn)而利用云模型來(lái)描述幾種ST段的不同特征。之后通過云變換以生成ST段判別規(guī)則云,將待檢測(cè)的ST段數(shù)據(jù)特征作為輸入,通過其對(duì)判別規(guī)則云隸屬度的判斷,來(lái)進(jìn)行ST段形態(tài)的判別。本研究利用歐盟CSE心電數(shù)據(jù)庫(kù)平臺(tái),采用Matlab對(duì)算法進(jìn)行測(cè)試,結(jié)果驗(yàn)證了該算法有效可行,利用云模型所得到的ST段判別結(jié)果符合醫(yī)學(xué)診斷邏輯思維。
[Abstract]:Acute myocardial infarction (AMI) is a common disease that endangers the health of middle-aged and old people. It is mainly detected by electrocardiogram in the early stage of the disease. At present, the effective diagnosis and prevention of the disease has become a new subject in the medical field. In the past, 12 leads were used in the conventional ECG acquisition system, but most of the patients were not in the hospital when the disease broke out. This study is based on the development of a portable, single-lead ECG monitoring cell phone. The algorithm of single lead ECG signal detection is studied, which enables patients to monitor and diagnose ECG at any time and anywhere, which has become a developing direction of ECG monitoring equipment. In the early stage of acute myocardial infarction (AMI), the changes of St segment are mainly reflected in the changes of St segment of electrocardiogram. Therefore, the correct recognition of St segment of ECG signal is of great significance for the diagnosis of acute myocardial infarction. St segment represents the potential changes in the repolarization process after ventricular depolarization is completed. It is easy to be interfered by external disturbance such as myocardial ischemia, noise and so on. In addition, the shape of St segment is different in different leads of the same body. Therefore, the maturity of automatic recognition of St segment is much lower than that of QRS wave recognition technology. It is difficult to measure the shape and level of St segment at the termination point, and there is no uniform measurement standard at present. In this paper, a novel ECG St detection method based on two-dimensional cloud model theory is proposed. 1) according to the characteristics of the noise in St segment, a 9-order Chebyshev band-pass digital filter is designed to filter and reduce the noise of ECG signal by using the zero-phase digital filter. The spectrum of the signal before and after filtering is analyzed. It is verified that the filter can effectively eliminate the low frequency baseline drift and power frequency interference under the condition that the ECG signal shape is not distorted (mainly St segment shape). In the aspect of extracting characteristic parameters of ECG signal, this paper uses differential threshold method to detect QRS wave of ECG signal, and synthetically uses time moving window and adaptive technology to improve detection accuracy. It overcomes the disadvantages of large computation of wavelet transform and long training time of neural network template, so it is more suitable for the development of portable single-lead ECG monitoring mobile phone. At the same time, local transform algorithm is used to extract St segment starting point and termination point. The simulation results show that the algorithm has high accuracy. 3) in view of the fuzzy and randomness of ECG data, this paper presents a method of ECG St segment detection based on cloud model, which can judge the membership degree of discriminant rule cloud by the signal to be tested. Firstly, the cloud model is used to cluster the frequency of a large number of sample points (potential value, first derivative and second derivative) in St segment. The St segment feature synthesis cloud with its own characteristics is obtained, and then the different features of several St segments are described by using cloud model, and then the St segment discriminant rule cloud is generated by cloud transformation, and the St segment data feature to be detected is used as input. By judging the membership degree of the discriminant rule cloud, the St segment shape is judged. In this study, the algorithm is tested by using the CSE ECG database platform of EU and Matlab. The results show that the algorithm is effective and feasible. The result of St segment discrimination obtained by cloud model accords with the logical thinking of medical diagnosis.
【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】：2012
【分類號(hào)】：TH776;R318.0

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