Heart Sound Localization in Respiratory Sounds Based on Singular Spectrum Analysis and Frequency Features

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.     

基于小波变换的心音信号降噪方法

为了弥补传统阈值函数在消噪过程中存在的不足,得到高信噪比的心音信号更好地进行心音分析,本文提出一种新的阈值函数。该函数通过灵活调节参数a和m的大小,更好地对染噪心音信号小波分解的每一层高频系数进行阈值量化。仿真中,应用传统的软、硬阈值函数及新阈值函数分别对大量的标准心音信号进行消噪处理,并对消噪效果进行了比较分析,同时将新阈值函数应用到实测心音信号消噪中。结果表明,新阈值函数能有效地消除噪声和保留心音信号的特征,具有较强的实用价值。     

基于光纤光栅传感器的心音信号提取与处理算法研究

本文研究了一种用于光纤光栅智能服装的心音信号提取与处理算法,实现异常心音的初步识别。提出基于希尔伯特-黄变换（HHT）和小波阈值消噪相结合的心音提取算法,对波长解调信号进行消噪,提取有用的心音信号。采用数学形态学进行心音包络提取,提出基于直线结构元素和余弦结构元素相结合的心音处理算法,准确获取心音峰值点和起止点位置并计算心音特征值,根据心音特征值的临床意义,判断心音是否正常。实验结果表明该算法能够有效消除波长解调信号中的呼吸干扰与噪声,对20例实测正常心音和8类常见异常心音均能正确识别。该算法具有易实现、识别率高的特点,对光纤传感智能服装的研发和心脏疾病的早期诊断具有重要意义。      

Reduced order Kalman filtering for the enhancement of respiratorysounds

In the processing and analysis of respiratory sounds, heart sounds present the main source of interference. This paper is concerned with the problem of cancellation of the heart sounds using a reduced-order Kalman filter (ROKF). To facilitate the estimation of the respiratory sounds, an autoregressive model is fitted to heart signal information present in the segments of the acquired signal which are free of respiratory sounds. The state-space equations necessary for the ROKF are then established considering the respiratory sound as a colored additive process in the observation equation. This scheme does not require a time alignment procedure as with the adaptive filtering-based schemes. The scheme is applied to several synthesized signals with different signal-to-interference ratios and the results are presented     

Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng

Auscultation of the chest is an attractive diagnostic method used by physicians, owing to its simplicity and noninvasiveness. Hence, there is interest in lung sound analysis using time and frequency domain techniques to increase its usefulness in diagnosis. The sounds recorded or heard are, however, contaminated by incessant heart sounds which interfere in the diagnosis based on, and analysis of, lung sounds. A common method to minimize the effect of heart sounds is to filter the sound with linear high-pass filters which, however, also eliminates the overlapping spectrum of breath sounds. In this work we show how adaptive filtering can be used to reduce heart sounds without significantly affecting breath sounds. The technique is found to reduce the heart sounds by 50?80 percent.     

Neural network based real-time heart sound monitor using a wireless wearable wrist sensor

A new method is presented using a wearable wrist sensor to estimate acoustic parameters S1 and S2 of the heart sounds based on the neural network technique. Using the signal processing method, the heart conditions can be analyzed and monitored in real time and potentially in a long term with a wrist device. The velocities and time delays of the cardiac pulse waves in blood vessels were experimentally acquired and calculated at different artery locations on the human body. Signal attenuation of the pulses from the heart to the wrist radial artery was analyzed and a pulse-waveform travel model in blood vessels was proposed. A band-pass filter is applied to the pulse waves at various artery locations to reveal the heart sound features S1 and S2 existed in the pulse waves. In order to obtain accurate acoustic parameters, a neural network with two layers and 500 nonlinear tansig neurons was employed to estimate the heart sounds using the pulse waveforms from the wrist radial artery. It is encouraging to find that the acoustic parameters of estimated heart sounds by the trained neural network have only 1% average errors compared with the original heart sounds. The effects of various analog-to-digital conversion resolutions and sample rates were empirically analyzed. When the maximum value of errors is allowed within 2.15%, a 10,000-Hz sample rate and 12-bit resolution should be an appropriate selection for lower power consumption. Using the trained neural network, the new estimation method has been verified by a sensor with Bluetooth communication strapped on the wrist, thus mobility is not limited for the person whose heart sounds need to be monitored.     

Interference cancellation in respiratory sounds via amultiresolution joint time-delay and signal-estimation scheme

This paper is concerned with the problem of cancellation of heart sounds from the acquired respiratory sounds using a new joint time-delay and signal-estimation (JTDSE) procedure. Multiresolution discrete wavelet transform (DWT) is first applied to decompose the signals into several subbands. To accurately separate the heart sounds from the acquired respiratory sounds, time-delay estimation (TDE) is performed iteratively in each subband using two adaptation mechanisms that minimize the sum of squared errors between these signals. The time delay is updated using a nonlinear adaptation, namely the Levenberg-Marquardt (LM) algorithm, while the function of the other adaptive system-which uses the block fast transversal filter (BFTF)-is to minimize the mean squared error between the outputs of the delay estimator and the adaptive filter. The proposed methodology possesses a number of key benefits such as the incorporation of multiple complementary information at different subbands, robustness in presence of noise, and accuracy in TDE. The scheme is applied to several cases of simulated and actual respiratory sounds under different conditions and the results are compared with those of the standard adaptive filtering. The results showed the promise of the scheme for the TDE and subsequent interference cancellation     

A comparative approach between cepstral features for human authentication using heart sounds

The main objective of this paper is to provide a comparative study between different cepstral features for the application of human recognition using heart sounds. In the past 10 years, heart sound, which is known as phonocardiogram, has been adopted for human biometric authentication tasks. Most of the previously proposed systems have adopted mel-frequency and linear frequency cepstral coefficients as features for heart sounds. In this paper, two more cepstral features are proposed. The first one is based on wavelet packet decomposition where a new filter bank structure is designed to select the appropriate bases for extracting discriminant features from heart sounds. The other is based on nonlinear modification for mel-scaled cepstral features. The four cepstral features are tested and compared on two databases: One consists of 21 subjects, and the other consists of 206 subjects. Based on the achieved results over the two databases, the two proposed cepstral features achieved higher correct recognition rates and lower error rates in identification and verification modes, respectively.     

低噪声电子听诊器的设计

心音、呼吸音是人体重要的声音信号,是医生进行心脏和呼吸系统疾病诊断的重要信息依据。而一般的听诊器引入的噪声较大,影响了诊断的正确性。为实现提高听诊的准确度,需要设计一种低噪声电子听诊器电路。此设计主要包括低噪声的前置级放大电路、滤波电路以及功率放大电路。通过这种低噪声电子听诊器的电路设计,实现了对心音和呼吸音的低噪提取,为实现对心音和呼吸音的正确诊断奠定了良好的基础。     

Noninvasive acoustical detection of coronary artery disease: acomparative study of signal processing methods

Previous studies have indicated that, during diastole, the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable. To detect such sounds, recordings of diastolic heart sound segments were analyzed using four signal processing techniques: the fast Fourier transform (FFT) autoregressive (AR), autoregressive moving-average (ARMA), and minimum-norm (eigenvector) methods. To further enhance the diastolic heart sounds and reduce background noise, an adaptive filter was used as a preprocessor. The power ratios of the FFT method and the poles of the AR, ARMA, and eigenvector methods were used to diagnose patients as having diseased or normal arteries using a blind protocol without prior knowledge of the actual disease states of the patients to guard against human bias. Of 80 cases, results showed that normal and abnormal records were correctly distinguished in 56 using the fast Fourier transform (FFT), in 63 using the AR, in 62 using the ARMA method, and in 67 using the eigenvector method. These results confirm that high-frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis     

Localizing heart sounds in respiratory signals using singular spectrum analysis

Respiratory sounds are always contaminated by heart sound interference. An essential preprocessing step in some of the heart sound cancellation methods is localizing primary heart sound components. Singular spectrum analysis (SSA), a powerful time series analysis technique, is used in this paper. Despite the frequency overlap of the heart and lung sound components, two different trends in the eigenvalue spectra are recognizable, which leads to find a subspace that contains more information about the underlying heart sound. Artificially mixed and real respiratory signals are used for evaluating the performance of the method. Selecting the appropriate length for the SSA window results in good decomposition quality and low computational cost for the algorithm. The results of the proposed method are compared with those of well-established methods, which use the wavelet transform and entropy of the signal to detect the heart sound components. The proposed method outperforms the wavelet-based method in terms of false detection and also correlation with the underlying heart sounds. Performance of the proposed method is slightly better than that of the entropy-based method. Moreover, the execution time of the former is significantly lower than that of the latter.     

Investigating the effects of vasodilator drugs on the turbulentsound caused by femoral artery stenosis using short-term Fourier andwavelet transform methods

The effects of vasodilator drugs on the turbulent sound generation mechanisms during femoral artery stenoses were investigated using the wavelet analysis of the turbulent sounds to characterize these sounds before and after the injection of vasodilator drugs. Results showed that the injection of drugs drastically improved the diagnostic performance of the turbulent sounds in detecting stenoses by increasing the signal-to-noise ratio of the sounds. Results also suggested that the sound above 250 Hz was drastically increased in response to the injection of the vasodilator drug for the partially occluded cases. The turbulence sounds caused by partially occluded femoral arteries are directly related to the slope of baseline of blood flow and to the velocity of the flow. For the 0% occlusion case, initially, sounds were produced with the injection of drugs. However, the sounds totally disappeared when the slope of average blood how was zero. These results show that the diagnostic performance of diastolic heart sounds associated with occluded arteries can be improved by using vasodilator drugs, which increase the acoustic energy in the first and second wavelet bandwidths due to the turbulence. The short-term Fourier transform (STFT) method was also applied to the same data base. Results using the STFT showed somewhat similar power distributions in that the acoustical power above 250 Hz was increased after the injection of drugs for the occluded cases. However, the WT method provided better time-frequency resolution than the STFT method, showing details of the change in the frequency characteristics with respect to time after the injection of drug     

基于SVM方法的神经网络呼吸音识别算法

提出了一种神经网络的SVM(支持向量机)呼吸音识别算法,将通过小波分析得到的呼吸音特征输入神经网络,作为SVM方法的特征输入,对训练样本进行训练,再对测试样本进行分类识别。对于呼吸音反映的3种状态（正常、轻度病变和重度病变）进行了识别,同时与K最近邻(KNN)方法进行比较。实验结果表明,SVM方法具有较高的识别精度,能够对呼吸音状态进行识别,同时在此领域也验证了在神经网络方法中无法避免的局部极值问题。提示基于SVM方法的神经网络呼吸音识别算法有较好的精度,可为身体局域网技术提供信息处理的有效算法。     

The synthesis of the aortic valve closure sound of the dog by themean filter of forward and backward predictor

The application of a new algorithm, the mean filter of forward and backward predictor, to synthesize the aortic component of the second heart sound (A2), according to an exponentially damped sinusoid model, is described. The resulting estimates of four modeling parameters composing each sinusoid of A2 (amplitude, damping factor, frequency, and phase) were used to synthesize the original aortic sounds. The synthesized sounds were then compared to the original sounds recorded on the thorax of six dogs and an error index was computed. The results show that the method is more precise than the forward predictor filter, the backward predictor filter, and the improved KT algorithm. The new algorithm is also highly stable and the error index between the original A2 and the synthesized waveforms is always less than 1%.     

Modified forward-backward overdetermined Prony method and itsapplication in modelling heart sounds

Prony's method is found to be a very effective method for the analysis-synthesis of transient data. However, straightforward application of this method can lead to poor performance, especially for short and noisy data records. The authors present a new over-determined forward-backward Prony method (MFBPM) and its application to the analysis of the first and second heart sounds. The accuracy of the method is measured using both cross-correlation and the normalised-mean-square-error (NMRSE) between a real signal and a synthetic one. Results from more than 80 different subjects show that the MFBPM is highly stable and gives very good performance with an average cross-correlation coefficient of 99.62%. Comparison of the results based on the NMRSE criterion show that the MFBPM is more precise than the modified backward Prony method (MBPM) with an accuracy improvement of upto 10%, and upto 20%, when compared with the conventional forward-backward Prony method (FBPM). Furthermore, a new method for dynamic estimation of model order is proposed for the case of heart sounds based on a subset of synthesised heart sounds which best approximates the observed data using NMRSE     

Heart-sound processing by average and variance calculation--physiologic basic and clinical implications

A new statistical method for heart-sound processing was developed and tested on normal subjects and on patients suffering from various cardiac pathologies. The method is effective in decreasing noise and in separating heart sounds from murmurs, as well as in deriving new physiological parameters. The theory is based on the assumption that heart sounds can be classified into deterministic and nondeterministic sounds. The processing results in a very significant attenuation of strong murmurs, while the deterministic events, such as SI-S4, are only slightly affected. The method includes dividing the heart-sound signal into a set of repetitive signals (ensemble) according to the trigger selected to be the peak of the ECG R-wave. The variability of the time elapsed from the trigger to the evoked sound is defined as the jitter. The average and variance functions are calculated from the ensemble. Calculation of the heartsound jitter from the average and variance functions shows a jitter of 5.5 ms ±2.6 ms for Si, and 8.2 ms ±3.3 ms for S2. The jitter, which is an objective parameter of the trigger-response linkage, can be used experimentally to clarify some of the cardiac electromechanical mechanisms, and it may have diagnostic value.     

A robust method for heart sounds localization using lung sounds entropy

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium 115 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 +/- 0.4% and 1.0 +/- 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.     

Optimal wavelet denoising for phonocardiograms

Phonocardiograms (PCGs), recordings of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analysed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG including foetal breath sounds if the subject is pregnant, lung and breath sounds, environmental noise and noise from contact between the recording device and the skin. An electronic stethoscope is used to record heart sounds and the problem of extracting noise from the signal is addressed via the use of wavelets and averaging. Using the discrete wavelet transform, the signal is decomposed. Due to the efficient decomposition of heart signals, their wavelet coefficients tend to be much larger than those due to noise. Thus, coefficients below a certain level are regarded as noise and are thresholded out. The signal can then be reconstructed without significant loss of information in the signal content. The questions that this study attempts to answer are which wavelet families, levels of decomposition, and thresholding techniques best remove the noise in a PCG. The use of averaging in combination with wavelet denoising is also addressed. Possible applications of the Hilbert transform to heart sound analysis are discussed.     

心音信号MFCC特征向量提取方法的优化

为了提高利用梅尔频率倒谱系数（Mel-Frequency Cepstral Coefficients, MFCC）特征向量进行心音信号分类的准确率，本文提出以一种基于独立成分分析（Independent Component Analysis, ICA）及权值优化的MFCC特征向量优化方法。首先，通过消除趋势项、降噪、提取心动周期与基础心音分割等步骤对心音信号预处理；接着，对提取的基础心音信号做Mel频谱变换及倒谱分析提取MFCC特征向量，其中用ICA替代离散余弦变换去除分量间高阶量的相关性，同时采用相关系数为权值优化整体混合矩阵；最后，采用F比衡量特征向量贡献率，并以其为权值优化各维特征向量。通过提取MFCC特征向量采用支持向量机（Support Vector Machine, SVM）的分类器识别第一心音及第二心音，并与人工标注心音状态集进行对比。实验结果表明，基于ICA及权值优化的MFCC特征向量在SVM分类器中识别率得到了有效的提升，且优化算法具备一定抗噪性能。