【摘要】：近年來(lái),中國(guó)的心血管疾病死亡率一直居所有疾病之首,患者人數(shù)在未來(lái)數(shù)年內(nèi)仍將繼續(xù)增加。心血管疾病在生活和經(jīng)濟(jì)上都給患者帶來(lái)了巨大的負(fù)擔(dān)。心電圖(ECG)可以直觀準(zhǔn)確地反映心臟的電活動(dòng)特性和表現(xiàn)心臟的工作狀態(tài),是目前臨床醫(yī)生判斷大部分心血管疾病的常用參考手段。但是,伴隨著心血管疾病患者人數(shù)的日益增加以及患者心電圖監(jiān)護(hù)數(shù)據(jù)的增加,完全讓臨床醫(yī)生人工根據(jù)心電圖來(lái)判斷心血管疾病發(fā)生將給醫(yī)生帶來(lái)巨大的工作負(fù)擔(dān),且很容易發(fā)生誤判和漏判。因此,心電自動(dòng)分析技術(shù)應(yīng)用于臨床心血管疾病的判斷逐漸成為當(dāng)前心電信號(hào)處理研究領(lǐng)域的熱點(diǎn)。多尺度熵(MSE)因其具有物理意義明確、分析更具有系統(tǒng)性等優(yōu)點(diǎn)在生物醫(yī)學(xué)信號(hào)處理領(lǐng)域正得到越來(lái)越多的應(yīng)用。本文基于多尺度熵對(duì)充血性心衰(CHF)和房顫(AF)兩種常見(jiàn)心臟疾病進(jìn)行了特征研究,并提出了一種基于多尺度熵的充血性心衰判別算法和一種基于多尺度熵的房顫判別算法。本文主要研究?jī)?nèi)容如下:(1)基于多尺度熵進(jìn)行了充血性心衰的特征研究,并比較了充血性心衰患者和正常人心率變異性之間的差異,發(fā)現(xiàn)健康人的多尺度熵的平均值大于充血性心衰患者,這說(shuō)明充血性心衰患者心電信號(hào)的復(fù)雜度低于健康人。最后本文基于多尺度熵并結(jié)合連續(xù)相鄰兩個(gè)RR間期之間差值的均方根提出了一種新的充血性心衰判別算法,我們用MIT-BIH心電數(shù)據(jù)庫(kù)中的心電數(shù)據(jù)驗(yàn)證了本文算法的性能。實(shí)驗(yàn)結(jié)果表明本文算法的判斷準(zhǔn)確率達(dá)到了91.67%,說(shuō)明本文算法具有一定的臨床應(yīng)用前景。(2)基于多尺度熵進(jìn)行了房顫的特征研究,并比較了房顫患者和正常人心率變異性之間的差異,發(fā)現(xiàn)健康人的多尺度熵的平均值大于房顫患者,這說(shuō)明充血性心衰患者心電信號(hào)的復(fù)雜度低于健康人。接著本文基于多尺度熵和信號(hào)功率譜低頻段能量與高頻段能量的比值兩個(gè)參數(shù),設(shè)計(jì)了一種新的房顫判別算法。最后,我們用MIT-BIH心電數(shù)據(jù)庫(kù)中的心電數(shù)據(jù)驗(yàn)證了本文算法的性能。實(shí)驗(yàn)結(jié)果表明本文算法的準(zhǔn)確率、敏感度和陽(yáng)性預(yù)測(cè)率分別為93.06%、91.67%和94.29%。
[Abstract]:In recent years, the death rate of cardiovascular disease in China has been the first of all diseases, and the number of patients will continue to increase over the next few years. Cardiovascular disease has a great burden on patients both in life and in the economy. Electrocardiogram (ECG) can directly and accurately reflect the electrical activity characteristics of the heart and the working state of the heart, and is a common reference for the current clinician to judge most of the cardiovascular diseases. However, with the increase of the number of patients with cardiovascular disease and the increase of the patient's ECG monitoring data, the clinician is completely allowed to manually judge the occurrence of the cardiovascular disease according to the electrocardiogram, which will cause great work burden to the doctor, and can be easily misjudged and missed. Therefore, the application of the automatic electrocardio-analysis technique to the clinical cardiovascular disease is becoming a hot spot in the current research field of ECG signal processing. The multi-scale entropy (MSE) is getting more and more applications in the field of biomedical signal processing because it has the advantages of clear physical meaning, more systematic analysis and the like. In this paper, the characteristics of two common heart diseases of congestive heart failure (CHF) and atrial fibrillation (AF) are studied based on the multi-scale entropy, and a multi-scale entropy-based judgment algorithm for congestive heart failure and an AF discrimination algorithm based on multi-scale entropy are presented. The main contents of this paper are as follows: (1) The characteristic study of congestive heart failure is carried out based on the multi-scale entropy, and the difference between the heart rate variability of the patients with congestive heart failure and the normal person is compared, and the average value of the multi-scale entropy of the healthy person is found to be larger than that of the patients with congestive heart failure. This indicates that the complexity of the cardiac electrical signal in patients with congestive heart failure is lower than that of a healthy person. In the end, based on the multi-scale entropy and combined with the root mean square of the difference between two consecutive RR intervals, a new algorithm for the determination of congestive heart failure is presented, and the performance of this algorithm is verified by the ECG data in the MIT-BIH ECG database. The experimental results show that the accuracy of the algorithm is 91.67%, which shows that the algorithm has a certain clinical application prospect. (2) Based on the multi-scale entropy, the characteristics of the atrial fibrillation were studied, and the difference between the heart rate variability of the patients with atrial fibrillation and the normal person was compared, and the mean value of the multi-scale entropy of the healthy person was found to be larger than that of the patients with atrial fibrillation, indicating that the complexity of the cardiac electrical signal in the patients with congestive heart failure was lower than that of the healthy person. Then, based on the multi-scale entropy and the ratio of the low-band energy of the signal power spectrum and the energy of the high-frequency band, a new algorithm for the determination of AF is designed. Finally, we use the ECG data in the MIT-BIH ECG database to verify the performance of the algorithm. The results show that the accuracy, sensitivity and positive predictive rate of the algorithm are 93.06%, 91.67% and 94.29%, respectively.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R540.4

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