【摘要】：近年來,中國的心血管疾病死亡率一直居所有疾病之首,患者人數(shù)在未來數(shù)年內仍將繼續(xù)增加。心血管疾病在生活和經濟上都給患者帶來了巨大的負擔。心電圖(ECG)可以直觀準確地反映心臟的電活動特性和表現(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)護數(shù)據(jù)的增加,完全讓臨床醫(yī)生人工根據(jù)心電圖來判斷心血管疾病發(fā)生將給醫(yī)生帶來巨大的工作負擔,且很容易發(fā)生誤判和漏判。因此,心電自動分析技術應用于臨床心血管疾病的判斷逐漸成為當前心電信號處理研究領域的熱點。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)點在生物醫(yī)學信號處理領域正得到越來越多的應用。本文基于多尺度熵對充血性心衰(CHF)和房顫(AF)兩種常見心臟疾病進行了特征研究,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。本文主要研究內容如下:(1)基于多尺度熵進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,這說明充血性心衰患者心電信號的復雜度低于健康人。最后本文基于多尺度熵并結合連續(xù)相鄰兩個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)庫中的心電數(shù)據(jù)驗證了本文算法的性能。實驗結果表明本文算法的判斷準確率達到了91.67%,說明本文算法具有一定的臨床應用前景。(2)基于多尺度熵進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,這說明充血性心衰患者心電信號的復雜度低于健康人。接著本文基于多尺度熵和信號功率譜低頻段能量與高頻段能量的比值兩個參數(shù),設計了一種新的房顫判別算法。最后,我們用MIT-BIH心電數(shù)據(jù)庫中的心電數(shù)據(jù)驗證了本文算法的性能。實驗結果表明本文算法的準確率、敏感度和陽性預測率分別為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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R540.4

【參考文獻】

相關期刊論文 前10條

1 陳偉偉;高潤霖;劉力生;朱曼璐;王文;王擁軍;吳兆蘇;李惠君;顧東風;楊躍進;鄭哲;蔣立新;胡盛壽;;《中國心血管病報告2015》概要[J];中國循環(huán)雜志;2016年06期

2 張媛;劉澄玉;董冰媛;崔紅梅;劉常春;;RR間期序列歸一化直方圖對CHF心功能級別評定的意義[J];實用醫(yī)藥雜志;2012年09期

3 宋節(jié)潔;劉琳;黎莉;;短時非線性心率變異性對心力衰竭患者心功能的評價[J];山東大學學報(醫(yī)學版);2012年01期

4 周星彤;周宇政;劉暢;謝明亮;;心率變異性分析新進展[J];中國醫(yī)藥指南;2011年23期

5 李佩;申岱;賈曉寧;;散點圖用于心率變異性分析的研究進展[J];國際麻醉學與復蘇雜志;2011年01期

6 張劍;萬穎;;關于心率變異性與年齡關系的研究[J];中國民康醫(yī)學;2010年18期

7 伍金莉;劉杰;;基于多分辨率分析的心率變異性分析[J];中國醫(yī)學物理學雜志;2010年01期

8 和衛(wèi)星;陳曉平;邵s，

本文編號：2510231