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.     

小波和神经网络在心音识别中的应用

提出一种心音的特征提取和分类方法,用离散小波变换分解、重构产生信号的细节包络,进而用于提取特征,从预处理的信号中提取统计特性,作为心音分类的特征。多层感知器用于心音的分类,并通过250个心动周期得到验证,算法识别率达到92%。     

基于深度卷积神经网络的心音分类算法

针对现有心音分类算法普适性差、依赖于对基本心音的精确分割、分类模型结构单一等问题,提出采用大量未经过精确分割的心音二维特征图训练深度卷积神经网络(CNN)的方法;首先采用滑动窗口方法和梅尔频率系数对心音信号进行预处理,得到大量未经过精确分割的心音特征图;然后利用深度CNN模型对心音特征图进行训练和测试;根据卷积层间连接方式的不同,设计了 3种深度CNN模型:基于单一连接的卷积神经网络、基于跳跃连接的卷积神经网络、基于密集连接的卷积神经网络;实验结果表明,基于密集连接的卷积神经网络比其他两种网络具备更大的潜力;与其他心音分类算法相比,该算法不依赖于对基本心音的精确分割,且在分类准确率、敏感性和特异性方面均有提升.     

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.     

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.     

Heart sound as a biometric

In this paper, we propose a novel biometric method based on heart sound signals. The biometric system comprises an electronic stethoscope, a computer equipped with a sound card and the software application. Our approach consists of a robust feature extraction scheme which is based on cepstral analysis with a specified configuration, combined with Gaussian mixture modeling. Experiments have been conducted to determine the relationship between various parameters in our proposed scheme. It has been demonstrated that heart sounds should be processed within segments of 0.5 s and using the full resolution in frequency domain. Also, higher order cepstral coefficients that carry information on the excitation proved to be useful. A preliminary test of 128 heart sounds from 128 participants was collected to evaluate the uniqueness of the heart sounds. The HTK toolkit produces a 99% recognition rate with only one mismatch. Next, a more comprehensive test consisting almost 1000 heart sounds collected from 10 individuals over a period of 2 months yields a promising matching accuracy of 96% using the proposed feature and classification algorithm. A real-time heart sound authentication system is then built and can be used in two modes: to identify a particular individual or to verify an individual's claimed identity.     

Heart sound classification based on scaled spectrogram and tensor decomposition

Heart sound signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. However, automatic heart sound classification is still a challenging problem which mainly reflected in heart sound segmentation and feature extraction from the corresponding segmentation results. In order to extract more discriminative features for heart sound classification, a scaled spectrogram and tensor decomposition based method was proposed in this study. In the proposed method, the spectrograms of the detected heart cycles are first scaled to a fixed size. Then a dimension reduction process of the scaled spectrograms is performed to extract the most discriminative features. During the dimension reduction process, the intrinsic structure of the scaled spectrograms, which contains important physiological and pathological information of the heart sound signals, is extracted using tensor decomposition method. As a result, the extracted features are more discriminative. Finally, the classification task is completed by support vector machine (SVM). Moreover, the proposed method is evaluated on three public datasets offered by the PASCAL classifying heart sounds challenge and 2016 PhysioNet challenge. The results show that the proposed method is competitive.     

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。     

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。     

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time-scale domains

Biometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT-11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de-noising of cardiac sounds using multiresolution-decomposition and multiresolution-reconstruction (MDR-MRR). The de-noised signal is then segmented based on frame-windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel-frequency) and time-scale (T-S) domain (based on Wavelet Transform) features are extracted from the de-noised signal after segmentation. The features, extracted from the Cp-domain and the T-S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K-nearest neighbor (KNN). The performance of the classifications is assessed based on the k-fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big-O measurements. The T-S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T-S domain.     

A novel cuboid method with particle swarm optimization for real-life noise attenuation from heart sound signals

A novel cuboid method with particle swarm optimization (PSO) is proposed to attenuate real-life noise from heart sound (HS) signals. Firstly, the quasi-cyclic feature of HS is explored. It is found that for each cycle of HS, the fragmental signals at similar time section have similar frequency and energy. Based on this finding, short-time Fourier transform (STFT) is employed to decompose each HS cycle into time–frequency fragments which are called granules. Next, a cuboid is built for each granule to identify and see if it is a constituent of HS or noise. The dimensions of cuboid’s length, width, and height are optimized by PSO. An objective function of PSO based on the normalized autocorrelation coefficient is proposed. Then, granules representing HS are retained and merged into noise-quasi-free HS signal. The proposed de-noising method is assessed using mean square error (MSE) and compared with the recently proposed wavelet multi-threshold method (WMTM) and Tang’s method. The experimental results show that the proposed method not only filters HS signal effectively but also well retains its pathological information.     

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.     

IMF复杂度特征在心音信号分类识别中的应用

为提高非线性、非平稳心音信号特征提取的准确性和分类识别的高效性,提出一种基于固有模态函数（Intrinsic Mode Function,IMF）复杂度和二叉树支持向量机（Binary Tree Support Vector Machine,BT-SVM）的心音分类识别方法。对心音进行经验模式分解（Empirical Mode Decomposition,EMD）,得到若干反映心音本体特征的平稳IMF分量;利用互相关系数准则对其筛选,计算所选IMF分量的复杂度值为信号的特征;将其组成特征向量输入到BT-SVM进行分类识别。临床数据仿真结果表明,该方法能有效提取心音特征,与传统识别方法相比,具有训练时间短,识别率高等优点。     

Heart Rate Extraction from Vowel Speech Signals

This paper presents a novel non-contact heart rate extraction method from vowel speech signals.The proposed method is based on modeling the relationship between speech production of vowel speech signals and heart activities for humans where it is observed that the moment of heart beat causes a short increment(evolution) of vowel speech formants.The short-time Fourier transform(STFT) is used to detect the formant maximum peaks so as to accurately estimate the heart rate.Compared with traditional contact pulse oximeter,the average accuracy of the proposed non-contact heart rate extraction method exceeds 95%.The proposed non-contact heart rate extraction method is expected to play an important role in modern medical applications.     

基于双门限的第一、第二心音自动识别方法

为提高心音检测算法对异常心音的识别率,提出一种基于双门限的第一心音(S1)和第二心音(S2)自动识别方法,通过海明窗进行滤波预处理,采用改进型希尔伯特-黄变换提取心音包络,利用双门限法对心音进行分段,使用临床知识对S1和S2进行自动识别。实验结果表明,该方法能够准确识别正常心音和异常心音中的S1和S2。     

A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.     

基于功率谱密度与卷积神经网络的心音分类

正常与异常心音分类在心血管疾病的筛查中有着重要的作用。建立在无心音分割的基础上,提出了一种基于功率谱密度时频分布特征与卷积神经网络的心音分类方法。该方法采用小波降噪做预处理,通过循环自相关获取心动周期,采用双线性插值法提取维度一致的心动周期功率谱密度时频特征,并送入卷积神经网络进行训练与测试。实验采用Challenge 2016数据集进行训练与测试,测试集的分类精度达到0.847 2,灵敏度和特异性评分达到0.776 3和0.946 3,整体性能良好。与其他算法的对比结果显示,该算法获得了更高的总体评分。     

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.     

类独立特征提取法在环境声音识别中的应用

语音和非语音类声音的识别在很多系统的研发中都有非常重要的作用,如安全监控、医疗保健、现代化的视听会议系统等。虽然绝大多数声音信号都有其独特的发音机制,然而要从其中进行特征的提取往往缺乏系统有效的方法。基于不同的音频信号都有其固有的特点,使用类所属特征选择方法来提取音频中的特征,从而进行分类,并用所提出的方法对语音和两种非语音类声音（咳嗽和杯碟破碎的声音）进行了实验仿真,实验结果表明,与常规的特征选择方法相比,提出的方法用更少的特征实现了更好的分类。