【作者】 赵杨；

【导师】 宗容； 王威廉；

【作者基本信息】 云南大学 ， 通信与信息系统， 2017， 硕士

【摘要】 心音是一种包含大量心脏活动信息的生物医学信号,它能够如实地反映心脏运行状态,通常作为先心病的主要诊断依据。目前对先心病主要的筛选方式是依靠心脏听诊。心脏听诊是由专业的医务人员借助心音听诊器来进行听诊,这就使得听诊结果受到听诊人员主观的影响,容易造成误诊。对于分布在医疗水平偏低的边远地区的大多数患者来说,只能通过心脏听诊来进行筛查。因此,对心音信号进行分析和处理对于诊断与心脏有关的疾病有重要的意义而且可以为临床诊断提供一些参考。本文的主要研究内容是基于Matlab对临床上采集到的心音信号进行处理,主要内容包括:1.心音信号的降噪。利用小波阈值去噪方法来消除心音信号中的噪声,并且通过实验比较了不同小波基的选取对去噪结果的影响。最后,通过原始心音与去噪后心音信号的实验对比,说明该去噪方法实现了去噪的效果。2.包络提取与分段定位。采用希尔伯特黄变换提取去噪后的信号包络,提出了一种新的自动分段定位的方法,使用双阈值分段方法。实验证明,新方法的分段正确率能达到90%以上。3.特征提取。使用了一种新的特征提取的方法,提取预处理后心音信号的Mel频率倒谱参数作为特征参数,通过后面分类识别实验表明,该参数在心音信号识别时能获得很好的效果。4.分类识别。对提取到的特征参数采用生物识别中应用得比较广泛的高斯混合模型(GMM)进行模式识别。本文对临床上采集到的心音信号进行了全面的研究,并采用高斯混合模型对信号中的正常和异常的信号进行了分类识别。实验结果证明,该系统有较好的识别效果,识别率能够达到80%。更多还原

【Abstract】 Heart sound is a kind of biomedical signal which contains a lot of cardiac activity information,it can faithfully reflect the heart running state,usually it is the main diagnosis basis of congenital heart disease.At present,the main screening method for congenital heart disease is to rely on heart sounds auscultation.Heart sound auscultation by a professional medical staff with a heart sound stethoscope to auscultation,which makes auscultation results subjective auscultation subjective influence,easily lead to misdiagnosis.For most patients in rural areas,screening can only be done by heart-hearing auscultation.Therefore,the analysis of the heart sound signal for the diagnosis of heart-related diseases is of great significance and can provide some reference information for clinical diagnosis.The main research contents of this paper are based on Matlab to deal with the heart sound signal,the main contents include:1.Heart sound signal denoising.Wavelet threshold denoising method is used to eliminate the noise in the heart sound signal,and the influence of different wavelet bases on the denoising result is compared by experiment.Finally,the denoising method achieves the effect of denoising by comparing the original heart sound with the auditory heartbeat signal.2.Envelope extraction and segmentation.A new automatic segmentation method is proposed by using Hilbert transform(HHT)to extract the signal envelope after denoising.A double threshold segmentation method is proposed.Experiments show that the new method of segmentation accuracy rate can reach more than 90%.3.Feature extraction.Using a new feature extraction method,extraction heart sound signal of preprocess Mel frequency cepstrum parameters as the characteristic parameter.The result of the following classification shows that the characteristic parameter can obtain good results in the heart sound signal recognition.4.Classification recognition.The Gaussian mixture model(GMM),which is widely used in biometrics,is used to identify the extracted feature parameters.In this paper,the heart sound signal collected in clinical was studied comprehensively,and the Gaussian mixture model was used to classify the normal and abnormal signals in the signal.The experimental results show that the system has better recognition effect,recognition rate can reach about 80%.更多还原