A novel approach for phonocardiographic signals processing to make possible fetal heart rate evaluations

In this paper, a non-invasive, portable and inexpensive antenatal care system is developed using fetal phonocardiography. The fPCG technique has the potential to provide low-cost and long-term diagnostics to the under-served population. The fPCG signal contains valuable diagnostic information regarding fetal health during antenatal period. The fPCG signals are acquired from the maternal abdominal surface using a wireless data acquisition and recording system. The diagnostic parameters e.g., baseline, variability, acceleration and deceleration of the fetal heart rate are derived from the fPCG signal. A model based on adaptive neuro-fuzzy inference system is developed for the evaluation of fetal health status. To study the performance of the developed system, experiments were carried out with real fPCG signals under the supervision of medical experts. Its performance is found to be in close proximity with the widely accepted Doppler ultrasound based fetal monitor results. The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio. The system can be used as first screening tool by the medical practitioners.     

Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps

Heart sound classification, used for the automatic heart sound auscultation and cardiac monitoring, plays an important role in primary health center and home care. However, one of the most difficult problems for the task of heart sound classification is the heart sound segmentation, especially for classifying a wide range of heart sounds accompanied with murmurs and other artificial noise in the real world. In this study, we present a novel framework for heart sound classification without segmentation based on the autocorrelation feature and diffusion maps, which can provide a primary diagnosis in the primary health center and home care. In the proposed framework, the autocorrelation features are first extracted from the sub-band envelopes calculated from the sub-band coefficients of the heart signal with the discrete wavelet decomposition (DWT). Then, the autocorrelation features are fused to obtain the unified feature representation with diffusion maps. Finally, the unified feature is input into the Support Vector Machines (SVM) classifier to perform the task of heart sound classification. Moreover, the proposed framework is evaluated on two public datasets published in the PASCAL Classifying Heart Sounds Challenge. The experimental results show outstanding performance of the proposed method, compared with the baselines.     

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.     

胎心音检测系统中滤波电路的设计

胎心音检测在胎儿监护中占有极其重要的地位。胎心音的主要频率范围集中在60~180Hz,因此在对信号进行A/D采样之前,应先设计滤波电路,滤除50Hz工频的谐波分量以及母体噪音。滤波电路采用OPA2277双运放芯片,设计具有正反馈的双T型陷波器滤除工频谐波,利用巴特沃斯滤波原理设计高通滤波器和低通滤波器滤除母体噪音。通过Mu ltisim软件对所设计的陷波电路和滤波电路进行频响特性仿真,得到的频响特性曲线表明:所设计电路可成功滤掉工频干扰和母体噪音干扰。     

An advanced method in fetal phonocardiography

The long-term variability of the fetal heart rate (FHR) provides valuable information on the fetal health status. The routine clinical FHR measurements are usually carried out by the means of ultrasound cardiography. Although the frequent FHR monitoring is recommendable, the high quality ultrasound devices are so expensive that they are not available for home care use. The passive and fully non-invasive acoustic recording called phonocardiography, provides an alternative low-cost measurement method. Unfortunately, the acoustic signal recorded on the maternal abdominal surface is heavily loaded by noise, thus the determination of the FHR raises serious signal processing issues. The development of an accurate and robust fetal phonocardiograph has been since long researched. This paper presents a novel two-channel phonocardiographic device and an advanced signal processing method for determination of the FHR. The developed system provided 83% accuracy compared to the simultaneously recorded reference ultrasound measurements.     

基于STC单片机和LabVIEW的心音信号检测系统

介绍了一种用于身份识别的心音信号采集和处理系统。该系统利用STC12C5A单片机作为核心控制器,通过自带的A/D对经过放大、滤波等预处理后的心音信号进行模数转换,然后通过RS232总线将信号传输到上位机进行处理,在上位机利用LabVIEW设计一套集数据采集、存储、回放和分析于一体的虚拟检测平台。实验结果表明,利用该系统采集三位被测试者60组心音信号,建立WPT+GA-SVM心音身份识别模型,其识别准确率达到了85%。     

基于自适应阈值的心音分段方法仿真研究

针对心音信号频率低、易受干扰并含有大量杂音的特点,提出了一种心音分段新方法。首先,采用dB6小波进行5层小波分解做心音信号降噪处理;然后,采用了一种提取降噪后心音信号连续平均能量包络的方法;最后,根据连续平均能量包络及自相关函数提出了一种自适应阈值心音分段方法。仿真结果表明,该方法所提取的心音信号包络特征更稳健,提出的分段算法与基于短时能熵比法和短时自相关函数法等心音分段算法相比,本文所提算法分段准确度更高。     

胎儿远程监护信息的采集与分析

目的:探讨胎儿远程监护系统的数据采集和胎儿信号分析的新方法。方法:采用声卡并结合Matlab软件实现对胎儿心音信号的采集,并分别运用自相关算法和自适应算法对信号进行分析。结果:数据的采集和分析效果理想,可以准确得到胎儿的心率。结论:该数据采集和信号分析方法应用于胎儿远程监护系统可以准确判断胎儿的健康状况,从而保障孕产妇和胎儿的安全。     

基于虚拟仪器的心音采集与分析系统设计

开发了一种新型的电子心音信号采集与分析系统,该系统以心音传感器和计算机自带声卡为基础,实现了心脏听诊从传统单一的"听"转变为可视、可听的多角度分析,结合LabVIEW和Matlab强大的数据分析能力实现了心音信号的采集、去噪、保存、分析等功能,可作为临床心脏诊断的辅助设备。     

Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems

In this study, a new scheme was presented for the prediction of fetal state from fetal heart rate (FHR) and the uterine contraction (UC) signals obtained from cardiotocogram (CTG) recordings. CTG recordings are widely used in pregnancy and provide very valuable information regarding fetal well-being. The information effectively extracted from these recordings can be used to predict pathological state of the fetus and makes an early intervention possible before there is an irreversible damage to the fetus. The proposed scheme is based on adaptive neuro-fuzzy inference systems (ANFIS). Using features extracted from the FHR and UC signals, an ANFIS was trained to predict the normal and the pathological state. The method was tested with clinical data that consist of 1,831 CTG recordings. Out of these 1,831 recordings, 1,655 of them were classified as normal and the remaining 176 were classified as pathological by a consensus of three expert obstetricians. It was demonstrated that the ANFIS-based method was able to classify the normal and the pathologic states with 97.2 and 96.6 % accuracy, respectively.     

基于智能手机的心音监测系统的研究与设计

心音监测对心脏疾病的检测和预防有重要作用,研究并设计基于智能手机的心音监测系统。该系统由心音测量节点、智能手机节点、服务器端组成。测量节点实现心音信号的采集,智能手机节点接收测量节点的心音并将其经GPRS网络传递到服务器端,服务器端对心音进行远程的监测。其中智能手机节点是该系统的枢纽,其主要功能是分析处理心音：对心音进行小波去噪处理,通过LZ复杂度算法获取心音的特征指标,将特征指标用于心功能的分析评价及其异常预警。通过功能性测试表明该系统能稳定运行。     

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。     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Feature selection using swarm-based relative reduct technique for fetal heart rate

Fetal heart rate helps in diagnosing the well-being and also the distress of fetal. Cardiotocograph (CTG) monitors the fetal heart activity to estimate the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of fetal heart rate signal, uterine contraction activity and fetal movements. Generally CTG comprises more number of features. Feature selection also called as attribute selection is a process of selecting a subset of highly relevant features which is responsible for future analysis. In general, medical datasets require more number of features to predict an activity. This paper aims at identifying the relevant and ignores the redundant features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using unsupervised particle swarm optimization (PSO)-based relative reduct (US-PSO-RR) and compared with unsupervised relative reduct and principal component analysis. The proposed method is then tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies–Bouldin index and Xie–Beni index for minimum number of classes. Empirical results show that the US-PSO-RR feature selection technique outperforms the existing methods by producing sensitivity of 72.72 %, specificity of 97.66 %, F-measure of 74.19 % which is remarkable, and clustering results demonstrate error rate produced by US-PSO-RR is less as well.     

深度卷积神经网络在心音分类方法中的应用

通过分析心音信号对心脏早期的病理状态进行确诊具有重要的意义。提出了一种基于深度卷积神经网络的心音分类方法。将心音信号转化成具有时频特性的梅尔频谱系数（Mel Frequency Spectral Coefficient,MFSC）特征图,将其作为深度卷积神经网络模型的输入;利用深度卷积神经网络对MFSC特征图进行训练,引入中心损失函数建立最优的深度学习模型;测试阶段,先将心音信号转换成多张二维MFSC特征图,然后利用训练好的深度学习模型对其分类,最后利用多数表决原则判断心音信号的类别。针对人工标注的训练样本有限,导致模型训练正确率不高的问题,以心音的二维MFSC特征图为对象分别从时间域和频率域进行随机屏蔽处理进而扩充训练样本。实验结果表明,该方法在PASCAL心音数据集上进行测试,对正常、杂音、早搏三种心音的分类性能明显优于现有最好的方法。     

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.     

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。     

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。      

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.