基于HHT和PPA的心音分段算法研究

心音是人体的一种重要的生理信号,它含有大量关于心脏病理状况的相关信息,反映了心脏及心血管结构及生理和病理信息。针对能否有效地提取第一心音(S1)、第二心音(S2),从而判断心脏是否病变,并且作为后续研究的基础,提出基于HHT和PPA的心音分段算法,包括首先利用希尔伯特-黄变换(HHT)进行心音包络的提取,然后利用中值滤波对包络进行平滑处理,最后通过峰逐层算法(PPA)来消除多余的低幅度峰值。通过对40例心音进行分段处理,可以对其中的39例进行正确分段。结果证明这种方法可以有效地提取心音信号的S1、S2,为后期的识别研究奠定了良好的基础。     

基于LMS-PNN算法在心音识别与预测中的应用

传统的概率神经网络（Probability neural network, PNN）具有很强的容错性、学习过程简单、训练速度快等特点。为提高传统PNN在心音分类方面的性能,利用最小均方(Least mean square, LMS)方法对其进行优化,进而提高心音分类与预测的准确性。LMS-PNN算法对心音的信号运用窗函数进行分帧,利用双门限法确定数据的值,运用LMS方法对相应的参数进行调试,并将去噪后的数据以mat格式保存,提取出各个心音的短时自相关系数以及短时功率谱密度,并运用PNN,抽取40 000个样本数据进行训练,并对各心音进行等级划分与预测。从PNN的模式层输入训练数据后,由实验数据验证可知,LMS-PNN算法的预测准确率可达96%以上。     

心音中医学指标的提取

根据心音信号自身的特点,结合经验模式分解,提出了一种从心音信号中提取医学指标的方法。对心音信号进行预处理,然后对预处理后的信号采用黄变换,获取各阶固有模态函数（IMFs）,从中选取第一和第二阶IMF进行希尔伯特变换,得到心音的包络。利用双阈值法,并给出具体的阈值,实现了对第一心音（S1）和第二心音（S2）的定位,从心音信号中获取了心率S1和S2的幅值比（S1/S2）以及舒张期和收缩期的时限比（D/S）,为临床上评估心脏储备提供了便利。     

基于支持向量机的心音信号自动识别

心音信号识别对心血管疾病的诊断具有重要意义,为了提高心音信号的识别性能,提出一种基于支持向量机的心音信号自动识别方法。首先采用小波分析对心音信号进行降噪预处理,然后提取心音信号的Mel频率倒谱系数作为心音信号特征,最后采用支持向量机建立心音信号分类器,对采集心音信号数据的识别性能进行验证。实验结果表明,本文方法的心音信号平均识别率高达93%以上,可以准确识别正常和各种异常的心音信号。      

基于可信度的双门限DMM协作频谱感知算法

针对已有的双门限特征值频谱感知算法存在忽略本地感知用户可靠性差异及融合判决方式开销大的缺点,提出了一种基于可信度的双门限DMM协作频谱感知算法(DT-CDMM),用于进一步提升协作感知性能。所提算法在最大最小特征值差(DMM)算法的基础上,建立了基于特征极限分布的双门限DMM算法作为本地感知,采用触发式的软、硬判决相结合的判决机制来减少系统开销,以本地感知性能与可信度加权的方式得到全局判决结果,并对硬判决进行自适应补偿。仿真结果表明, 较已有的双门限特征值算法以及双门限能量检测算法,DT-CDMM算法在噪声不确定的环境下提升了多用户协作检测的概率。     

基于D—S理论的双门限频谱感知算法研究

针对传统双门限频谱感知算法不能充分利用2个门限之间感知用户信息的问题,提出了一种基于D-S理论的双门限频谱感知算法。当感知用户接收到的信号能量介于2个门限之间时,该算法不直接给出判决结果,而是进行本地判决,即采用D-S理论进行数据融合。仿真结果表明,与传统双门限频谱感知算法相比,基于D-S理论的双门限频谱感知算法的检测概率较高,提高了整体的频谱感知性能。     

车载环境下语音端点检测的研究

语音端点检测直接决定了语音识别的精度和速度。车载环境是一个非常复杂的环境,信噪比(SNR)有可能出现很低的情况,对于传统的时域端点检测方法来说,在这种环境下的端点检测效果很差,而双门限在高信噪比条件下,端点检测的效果非常好,识别率很高,这就使得提高车载环境下语音SNR非常关键。文章提出采用改进的小波去噪和改进的双门限方法进行端点检测。实验结果表明,综合改进小波去噪和改进双门限的方法虽然有一定量的信号失真,但失真在可接受范围之内,并且在不增大运算量的情况下端点检测的效果比传统的双门限效果要好,表明了本文算法的有效性。     

基于双门限准则的飞行计划与雷达航迹配对

针对传统飞行计划与雷达航迹配对方法的缺陷和不足,提出基于双门限航迹关联思想的飞行计划与雷达航迹配对方法。利用飞行计划推算出计划航迹;基于双门限航迹相关思想对计划航迹和雷达航迹进行双门限准则判断,并以此判断结果作为飞行计划与雷达航迹配对的主要依据;通过多义性处理最终实现飞行计划与雷达航迹正确快速配对。通过仿真实验与其他方法进行比较验证:该算法具有较高的准确性和实时性。     

基于LMS-PNN神经网络算法在心音识别与预测中的应用

传统的PNN神经网络具有很强的容错性、学习过程简单、训练速度快等特点,本文在传统PNN神经网络的基础上,利用LMS对其在心音分类方面进行优化,进而提高心音分类与预测的准确性。LMS-PNN神经网络算法对心音的信号运用窗函数进行分帧,利用双门限法确定数据的值,运用LMS算法对相应的参数进行调试,并将去噪后的数据以mat格式保存,提取出各个心音的短时自相关系数以及短时功率谱密度,并运用PNN神经网络,抽取40000个样本数据进行训练,并将各个心音进行等级划分与预测。 从PNN神经网络的模式层输入训练数据后,通过仿真测试可得,LMS—PNN神经网络预测准确率可达可达96%以上。     

基于双门限的新型协作频谱感知算法

针对传统认知无线电双门限能量感知算法在可靠性和带宽受限上存在的不足,提出一种基于两比特硬合并的新型双门限协作频谱感知算法。该算法同时利用了认知用户的单比特局部判决结果和解决感知失败问题的两比特局部判决结果两种信息,并由融合中心结合两种局部判决信息做出最终判决从而确定主用户存在与否。仿真结果表明,与传统双门限算法相比,该算法仅以平均感知比特位略微提高为代价(感知失败概率较低时大约平均提高1%),不仅消除了感知失败问题,而且显著提高了感知性能(虚警概率较低时最大可提高21%)。     

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD

In this paper, a novel method was put forward for automatic identification of the normal and abnormal heart sounds. After the original heart sound signal was pre-processed, it was analyzed by the optimum multi-scale wavelet packet decomposition (OMS-WPD), and then the wavelet-time entropy was applied to extract features from the decomposition components. The extracted features were then applied to a support vector machine (SVM) for identification of the normal and five types of abnormal heart sounds. To show the robustness of the proposed method, its performance was compared with four other popular heart sound processing methods. Extensive experimental results showed that the feature extraction method proposed in this paper has convincing identification results, which could be used as a basis for further analysis of heart sound.     

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.     

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.     

基于能量检测的复杂环境下的鸟鸣识别

针对实际环境噪声使得鸟鸣识别准确率受到影响的问题,提出一种基于能量检测的抗噪鸟鸣识别方法。首先,对包含有噪声的鸟鸣信号用能量检测方法检测并筛选出有用鸟鸣信号;其次,根据梅尔尺度的分布,对有用鸟鸣信号提取小波包分解子带倒谱系数（WPSCC）特征;最后,用支持向量机（SVM）分类器分别对提取的小波包分解子带倒谱系数（WPSCC）和梅尔频率倒谱系数（MFCC）特征进行建模分类识别。同时还对比了在添加不同信噪比的噪声下15类鸟鸣在能量检测前后的识别性能差异。实验结果表明,提取的WPSCC特征具有较好的抗噪功能,且经过能量检测后的识别性能更佳,更适用于复杂环境下的鸟鸣识别     

Automatic heart sound detection in pediatric patients without electrocardiogram reference via pseudo-affine Wigner–Ville distribution and Haar wavelet lifting

Having in mind the availability of electronic stethoscopes, phonocardiograms (PCGs) have become popular for cardiovascular functionality monitoring and signal processing applications. Detection of fundamental heart sounds (HSs), S1s and S2s, is considered to be a crucial step in PCG analysis. Electrocardiogram (ECG), noted as a reference signal, is often synchronously recorded in order to simplify the S1/S2 detection process. Nevertheless, electronic stethoscopes are frequently used without additional ECG equipment. We propose a new algorithm for automatic fundamental HSs detection via: joint time-frequency representation based on pseudo affine Wigner–Ville distribution (PAWVD), Haar wavelet lifting scheme (Haar-LS), normalized average Shannon energy (NASE) and autocorrelation. The performance of the proposed algorithm was calculated on both normal (50) and pathological (75) PCG recordings, eight seconds long each, contributed by 125 different pediatric patients. The algorithm showed relatively high recall (90.41%) and precision (96.39%) rates of S1/S2 detection procedure in a variety of PCG signals, without ECG as a reference. Furthermore, it indicated the ability to overcome splitting within the S1/S2 heart sounds.     

HILBERT包络法在心音身份识别中的应用

在分析心音信号特征的基础上,对心音信号进行预处理,再利用希尔伯特变换对心音信号进行心音信号包络提取,突出了心音信号的第一心音和第二心音.然后对心音包络进行分段,通过单周期心音包络的归一化能量实现了心音信号的身份识别.     

基于循环平稳特性的心音信号噪声评价指标

采用计算机来分析心音信号引起了越来越多的研究人员的关注,但是,心音信号在采集过程中常常会受到各种噪声的干扰,如何评价心音信号受噪声影响的程度就成为一个重要的问题。提出了一种基于循环平稳特性的心音信号噪声评价指标--质量因子,它能够准确地、定量地评估心音信号的噪声情况,即质量因子越大,信号受噪声的影响越小。如果实际采集的心音信号比较长,那么计算整个信号的质量因子,把质量因子最大的那一段心音信号取出来进行处理,这样可以大大减少去除噪声等预处理过程,节省了计算量和时间。所提出来的质量因子,对于正常和异常心音信号都适用,计算机完全可以自动计算,无需人工干预。     

Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform

This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E_T) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E_T and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E_T can be expressed by multiplying the E_T by an equivalent window (W_E). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N = 1 s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%.