【作者】 梁崴巍；

【导师】 沙宪政；

【作者基本信息】 中国医科大学 ， 生物医学工程， 2009， 硕士

【摘要】 心血管疾病是人类死亡的主要原因之一,按世界卫生组织的统计,全世界每年死于心脏病的人约有1200万,占整个死亡人数的25%。随着现代人生活水平的不断提高,一些不健康的生活方式也会导致心脏病朝着年轻化发展。而心脏病特点是发病快,死亡率高,治疗难度大,治疗费用高。为了克服心脏病对人类的危害,早期发现,早期诊断,早期治疗可以显著降低死亡率。而家庭监护为这一切提供了可能。在家庭监护中,方便的心电图记录和心电自动诊断是人们关心的重点。本论文针对家庭便携式监护仪拟研究心电特征的检测方法。心电图是心脏搏动的电位变化图。它的各种波形和间隔代表着心脏的电气特征,医生根据心电图的波形来判断心脏活动的异常,从而诊断心脏疾病,并且可以用来监测病人的身体状况,评估病情和治疗成效。因此,用计算机来分析心电图的特征波形就显得尤为重要,分析得是否准确和迅速直接关系到患者的治疗。QRS复合波是心电图中最明显的波也是心电信号分析的基础,QRS波的定位是否准确直接关系到后续的监测。每个人的QRS波的形态有所差异,并且同一个人的QRS波也根据个人状态而表现出很大的差异性,因此,准确监测QRS波存在很大的难度。如何既能保持检测的准确性又能保证检测的时效性是很多研究人员面临的课题。小波变换方法的出现为信号检测提供了全新的方法。小波变换具有多尺度,多分辨分析的特点,可将信号进行多尺度分解,并把不同频带的信号现实在分解的不同尺度上。这样可以根据不同信号信息所在的频带来进行具体的分析。本文首先介绍了心电图的一些相关内容,各种波形的特点以及噪声的种类。对本实验中所使用的MIT-BIH心电数据库和小波变换的相关基础知识也作了简单的介绍。本文详细说明了使用小波变换方法对心电图进行降噪处理和特征识别。首先,对心电图进行消噪操作,尝试使用多种方法进行降噪。然后,利用三次样条小波在心电图特征点识别方面的突出优势,对心电图进行8层分解,对R波识别和定位;在此基础上,找出R波的起止点的位置;并且定位P和T波的位置;确定RR间期。本文的重点是对R波进行准确的定位,计算RR间期和确定QRS波的起止点。并在此基础上尝试对P、T波进行检测。P、T波的检测是心电图检测的难点所在。最后,在算法评估中,我们将心电图原始数据与所检测到的数据相对比,进行统计分析,评估算法的准确率。更多还原

【Abstract】 Cardiac diseases are thought as a kind of major diseases which become more and more serious to human health.According to the Statistics of the WHO,it is about more than 1.2 million people died of Cardiac diseases,account for 25%of the total mortality. As the improving of the living standard,some unhealthy lifestyle would lead the diseases to the trend of rejuvenation.The characteristics of Cardiac diseases are:onset fast,high mortality rate,difficult to treat,expensive treatment cost.In order to overcome the danger of the diseases,early detection,diagnosis,and treatment will greatly decrease mortality rate.Family monitoring make it possible.In the family monitoring,to record the ECG conveniently and detect automatically are the most important.Electrocardiogram is a pictorial representation of the electrical activity of heart beats.Because of the direct relationship between the ECG waveform and interval of the heart beats,it is possible that doctor can diagnose cardiac disease and monitor patient conditions from the unususal ECG waveforms.QRS complex detection is the base of automatic analysis of ECG,which is mostly the first step of analysis and affects the following steps greatly.The difficulties of QRS detection lie in two factors:firstly,the physiological variability of QRS complex. Secondly,the various types of noises that can present in the ECG,for instance the power line noise,muscle noise,baseline drift.Using Multriesolution wavelet Analysis decomposed the ECG signal on multiscals, different frequency-bands of the signal were showed on different scales.Analysis signals in different frequency-bands.Based on the wavelet transform,this thesis introduces an algorithm to detect QRS complex.In particular,the quadratic spline wavelet has been adopted.The thesis first reviews wavelet transform briefly,and de-noise the ECG signal;then develops a QRS detention algorithm,which is then tested by using the MIH-BIH arrhythmia database. The result of experiments shows that the performance of models based on Quadratic Spline Wavelet is superior,and it can detect QRS complex exactly.We use this algorithm to identify and locate the R-Wave of the ECG.Based on the R-peak,find the R-start and R-end;and then locate the P-wave and T-wave;calculate the R-R intervals. The thesis emphasis on R-wave detection,R-R interval calculation,R-start and R-end determination.And then attempt to detect P-wave and T-wave.P and T detection is the nodus of ECG-detection.In the evaluation of the algorithm,contrast the detected data with original data,make statistical analysis,determine the accuracy of the algorithm.更多还原