基于小波分析和概率神经网络的心音诊断研究

心音对大多数心血管疾病具有极高的临床诊断价值,对心音信号进行分析有助于临床上对心脏疾病的诊断。为了利用计算机智能分析心音信号,提出利用多尺度小波分解消除信号中的噪声,从各频带提取特征值,用概率神经网络（PNN）来进行心音信号的自动分析诊断。用Matlab仿真的方法测试了5种不同类型心音信号的分类情况,结果表明该方法可行。     

BP神经网络应用于孤立词语发音识别的研究

介绍了BP神经网络的学习规则和用于语音识别的基本原理,建立了一个用于常用孤立词语音识别的BP神经网络,选择声道反射系数为语音识别的特征值,建立了网络的训练样本集,对网络进行了训练;用MATLAB进行了识别仿真,表明能较好地实现孤立词语音识别.     

心脏杂音提取和分类识别研究

为了分析心脏杂音中包含的病理信息,采用奇异谱主分量分析方法从病理心音信号中提取杂音成分。对四种常见的病理心音信号进行奇异谱分析,得到各主分量和经验正交函数,选择合适阶次重构正常心音成分和杂音成分。计算杂音信号的样本熵作为特征值输入支持向量机分类器实现分类识别,为临床诊断提供参考信息。     

基于高阶统计量的心音特征提取与分类研究

心脏听诊是先心病初诊的主要手段,由此可见心音含有重要的诊断信息.研究利用高阶统计量的ARMA模型直接提取先心病心音信号的特征,利用K近邻,决策树,贝叶斯分类器对其进行分类识别,并与用梅尔对数系数提取特征的方法进行了对比.实验测试结果为灵敏度0.88,特异度0.83,准确度0.855,优于其它算法.上述算法还省去了一般预...     

基于两级神经网络的心音分割

心音信号是分析诊断心脏疾病的重要信号,而心音分割是对其进行分析处理之前必不可少的一步。本文通过将心音分割任务分离为定位与识别两个子任务,提出一种两级卷积神经网络,由定位网络和判别网络两级构成,分别完成心音信号的识别与定位。首先将原始信号通过滑动窗口进行分帧,然后通过短时傅里叶变换得到其频谱,再通过梅尔滤波器得到其梅尔频谱系数（Mel frequency spectral coefficient, MFSC）特征,输入第1个定位网络对其是否为心音段进行判断,如果是的话,再输入判别神经网络,识别第一心音与第二心音,从而实现心音的分割。最后利用多帧结果投票,减小误判。同时,在卷积神经网络中引入空间注意力机制,实验结果表明,这种加入了注意力机制的两级神经网络模型在心音分割任务上比使用单个卷积神经网络分类模型的准确率更高,也使得模型更加简单,轻量化。     

基于小波变换和样本熵的心音识别研究*

讨论了一种非线性动力学与时频分析结合的心音信号分析方法,首先利用小波变换去除信号中的噪声干扰,然后提取心音信号的样本熵（SampEn）特征值。最后通过支持向量机分类器验证表明,该算法能有效提高心音信号分类识别的准确率,对实现疾病辅助诊断具有很大的价值。     

基于PNN网络和Freeman链码的抽油机井工况诊断

利用概率神经网络(PNN)对抽油机井工况进行诊断,建立了抽油机井工况诊断的概率神经网络模型;对示功图提取特征值的质量好坏直接影响识别效率和可靠性,提出了用Freeman链码对等效的电流示功图提取特征参数,进行预处理,建立抽油机典型工况的链码特征样本库;将Freeman链码作为特征向量,利用MATLAB对网络进行训练;结果表明,Freeman链码能够有效地识别各种典型工况示功图,并且该概率神经网络学习速度快、诊断准确率高,可用于抽油机井工况的实时监测和诊断.     

基于STM32可视化心音信号电子测试系统的研究

电子听诊技术在临床医学、工业探测等领域有着广泛应用,医学上心音听诊是诊断心脏等内脏疾病的主要手段之一,而传统听诊器因其固有缺陷,很难满足现代医学发展要求。以医学电子听诊为研究对象,选定STM32F107为中央处理器,结合微弱信号处理技术,设计了一套心音信号的采集分析系统,完成了心音信号高速、高精度、抗干扰、实时可视化听诊,并结合MATLAB仿真软件对采集到的心音信号进行分析,实现了实时心音信号采集、前端信号处理和分析,完成了数据压缩、存储、分类,以及波形及数据显示回放等功能,以满足临床医学听诊的要求。     

基于小波分析和神经网络的心音信号研究

针对传统的冠心病诊断方法具有不准性或有创性问题,积极广泛开展冠心病无损检测的研究,提高诊断准确性,为大众提供方便可行的检测手段是十分必要的。在分析冠状动脉堵塞与心音信号关系的基础上,研究心音信号的预处理,对心音信号进行去噪和定位分段;利用ARMA模型及功率谱估计对心音信号进行分析研究,提取冠心病病理特征;通过神经网络对心音信号进行分类,实现冠心病的智能无损诊断。实验结果表明,采用上述方法进行冠心病无损诊断准确率达到85.1%,为临床上的冠心病的无损诊断提供了应用基础。     

基于Freeman链码特征值的示功图分类识别研究

示功图分析是目前比较常用的油井故障诊断方法,基于神经网络的示功图分类识别要求准确地提取示功图的特征值,特征值的质量直接关系到示功图识别的效率和可靠性。传统的示功图特征值提取方法计算量很大,与油井现场的实时性要求相悖。为了解决这一问题,提出了用Freeman链码来表达示功图特征,对示功图的识别进行研究。分析了示功图Freeman链码的提取方法以及典型工况链码特征,建立示功图链码特征样本库,给出了示功图识别的方法步骤,在MAT-LAB下进行仿真验证。结果表明,Freeman链码特征值能够有效地分类出各种典型工况示功图,神经网络具有更快的收敛速度和更高的识别效率。     

Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy

Listening via stethoscope is a preferential method, being used by physicians for distinguishing normal and abnormal cardiac systems. On the other hand, listening with stethoscope has a number of constraints. The interpretation of various heart sounds depends on physician’s ability of hearing, experience, and skill. Such limitations may be reduced by developing biomedical-based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary, and mitral stenosis heart valve diseases via stethoscope. Developed system comprises of three stages. In the first stage, for feature extraction, obtained heart sound signals were separated to its sub-bands using discrete wavelet transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of three types of heart sound signals were used as input patterns of the adaptive neuro-fuzzy inference system (ANFIS) classifiers. Developed method reached 98.33% classification accuracy, and it was showed that purposed method is effective for detection of heart valve diseases.     

Heart sound screening in real-time assistive environments through MCMC Bayesian data mining

Emerging pervasive assistive environment applications for remote home healthcare monitoring of the elderly, disabled and also patients with various chronic diseases generate massive amounts of sensor signal data, which are transmitted from numerous homes to local health centers or hospitals. While it is critical to process this data efficiently (in a fast and accurate manner) and cost-effectively, in a large-scale application of the above technologies, it is not possible to do so manually by specialized human resources. This paper proposes a methodology for automatic real-time screening of heart sound signals (one of the most widely acquired signals from the human body for diagnostic purposes) and identification of those that are abnormal and require some action to be taken, which can be applied to many other similar types of bio-signals generated in assistive environments. It is based on a novel Markov Chain Monte Carlo Bayesian Inference approach, which estimates conditional probability distributions in structures obtained from a Tree-Augmented Naïve Bayes algorithm. It has been applied and validated in a highly ‘difficult’ heterogeneous dataset of 198 heart sound signals, which comes from both healthy medical cases and unhealthy ones having aortic stenosis, mitral regurgitation, aortic regurgitation or mitral stenosis. The proposed methodology achieved high classification performance in this difficult screening problem. It performs higher than other widely used classifiers, showing great potential for contributing to a cost-effective large-scale application of ICT-based assistive environment technologies.     

Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction

In this study, a biomedical system to classify heart sound signals obtained with a stethoscope, has been proposed. For this purpose, data from healthy subjects and those with cardiac valve disease (pulmonary stenosis (PS) or mitral stenosis (MS)) have been used to develop a diagnostic model. Feature extraction from heart sound signals has been performed. These features represent heart sound signals in the frequency domain by Discrete Fourier Transform (DFT). The obtained features have been reduced by a dimension reduction technique called principal component analysis (PCA). A discrete hidden Markov model (DHMM) has been used for classification. This proposed PCA-DHMM-based approach has been applied on two data sets (a private and a public data set). Experimental classification results show that the dimension reduction process performed by PCA has improved the classification of heart sound signals.     

便携式低功耗电子听诊器设计

设计基于电容式传感器,采用STM32F405RGT6作为主控芯片,结合了Bluetooth,OLED,SD卡存储等技术实现了在心音、肺音、宽频模式下的实时听诊、录音存储及回放、听诊音量调节及无线传输等功能.系统功能完善、便携性高、低功耗,有利于推动电子听诊器医用,同时也适用于听诊教学等多种场合.     

Automatic phonocardiograph signal analysis for detecting heart valve disorders

Skilled cardiologists probe heart sounds by electronic stethoscope through human ears, but interpretations of heart sounds is a very special skill which is quite difficult to teach in a structured way. Because of this reason, automatic heart sound analysis in computer systems would be very helpful for medical staffs. This paper presents a complete heart sound analysis system covering from the segmentation of beat cycles to the final determination of heart conditions. The process of heart beat cycle segmentation includes autocorrelation for predicting the cycle time of a heart beat. The feature extraction pipeline includes stages of the short-time Fourier transform, the discrete cosine transform, and the adaptive feature selection. Many features are extracted, but only a few specific ones are selected for the classification of each hyperplane based on a systematic approach. The experiments are done by a public heart sound database released by Texas Heart Institute. A very promising recognition rate has been achieved.     

基于小波变换多分辨率特征提取的模拟电路故障诊断方法研究

提出一种基于小波变换多分辨率特征提取的模拟电路故障诊断的方法。该方法先对采样后的故障信号进行小波分解,提取各频段系数作为特征向量输入到神经网络进行训练。通过带通滤波器电路诊断的实例,阐述该方法的具体实现,验证该方法可以有效地简化神经网络结构和减少它的训练时间,快速高效地进行模拟电路故障的诊断和定位。     

基于机器学习的肺音分类技术的研究进展

肺音(Lung Sound) 信号是人体呼吸系统与外界在换气过程中产生的一种生理声信号,其因含有大量的生理和病理信息而具有很高的研究价值。近年来,频发的雾霾天气等环境问题所带来的呼吸道疾病发病率的提高,也使得对肺部疾病诊断的快速性与准确性的需求大幅提升。肺部听诊以其迅捷便利和无创等优良特性重新引发人们的广泛关注,而自动肺音诊断技术的发展无疑会对肺部疾病诊断带来重要的帮助。电子听诊器以及其他信号采集技术等硬件方面的发展进一步促进了现代肺音信号的分析和识别技术的研究与进步。主要介绍了肺音的概念、基于计算机的肺音信号处理和模式识别技术,并对近年来基于机器学习的肺音分类技术的发展状况进行了总结与列举;最后,对肺音分类技术的研究和应用发展趋势进行了展望。     

基于PCA串并融合的多路心音特征表征方法

多路心音信号不仅比单路心音信号涵盖更多关于总体的特征,而且能够弥补单路心音数据携带的信息量可能不充分的缺陷。利用笔者自主设计的4路心音传感器,初步建立一个小型4路心音数据库。基于这个数据库,首先阐明多路心音信号的特点,论述心杂音与听诊位置的关系;然后分别提取心音的单路和4路能量熵系数、4路心音互信息作为有效特征数据集,利用PCA对能量熵特征进行降维处理,获得串行特征;将相关性特征和互信息特征从实向量空间拓展到复向量空间,进行并行融合,获得并行特征;最后将串行并行特征再次融合成为多元优化组合特征。这种融合策略,具有针对性强,凸显差异性的优点。仿真实验结果表明,由多路心音信号获取的多元优化组合特征表征效果明显优于单路心音信号的特征表征效果,不仅有益于分类模型的构建,而且对实现先心病的快速筛查,提高分类识别率具有积极的意义。     

Feature determination for heart sounds based on divergence analysis

Heart auscultation (the interpretation of heart sounds by a physician) is a fundamental component of cardiac diagnosis. It is, however, a difficult skill to acquire. In decision making, it is important to analyze heart sounds by an algorithm to give support to medical doctors. In this study, two feature extraction methods are comparatively examined to represent different heart sound (HS) categories. First, a rectangular window is formed so that one period of HS is contained in this window. Then, the windowed time samples are normalized. Discrete wavelet transform is applied to this windowed one period of HS. Based on the wavelet detail coefficients at several bands, the time locations of S1–S2 sounds are determined by an adaptive peak detector. In the first feature extraction method, sub-bands belonging to the detail coefficients are partitioned into ten segments. Powers of the detail coefficients in each segment are computed. In the second feature extraction method, the power of the signal in a window which consists of 64 samples is computed without filtering the HSs. In the study, performances of these two feature extraction methods are comparatively examined by the divergence analysis. The analysis quantitatively measures the distribution of vectors in the feature space.     

心音身份识别技术的研究

文中研究心音身份识别的基本原理和实现方法.首先分析心音信号的特性和作为生物识别的可行性;然后建立基于心音子波族的心音信号合成模型,并且用特征向量分布相图形象地比较两个心音的特征,用倒谱减法消除听诊器的类型和位置变化所产生的影响;最后,采用心音线性频带倒谱(HS-LBFC)提取心音特征参数,用相似距离等实现心音的身份识别.为了突出心音在时、频域上存在的差异,重点研究了构建心音子波的方法,合成模型中各参数的计算方法,以及心音特征参数的确定和对应的数据处理技术.实际实验结果表明,该方法具有很好的识别率和实用性.