A Study of Prosthetic Heart Valve Sounds

In this paper a new mechanism is proposed for the generation of phonocardiogram (PCG) sounds from implanted mechanical prosthetic heart valves. The structures in the chest, the heart, its partitions, and major vessels, constitute a frequency selective system excited by the rapidly decelerating valve occluder. It is shown that the source, the rapidly decelerating valve, has a wide and flat power spectrum and hence is an impulsive excitation that couples energy to the resonance modes specified by the structures in the chest. Consequently, the PCG signal is composed of decaying sinusoids. The parameters of the decaying sinusoids are estimated, and it is observed that the power spectra of the PCG signals have two dominant peaks in the frequency band of 200-500 Hz. The energy coupled to these two modes depends on the state of the valve. With thrombus the decelerating occluder slows down and becomes a broader pulse concentrating the energy to the lower resonance mode. This is verified by experiments on 30 patients during postoperative time course. However, no significant change in the resonance frequencies are observed which is an evidence for their anatomical and not valvular dependence.     

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.     

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.     

Modeling of the Transfer Function of the Heart?Thorax Acoustic System in Dogs

A computer model capable of reflecting the dynamic bevior of the heart-thorax acoustic system is used to study the transmission of the first and second heart sounds originating within the left ventricle through the heart muscle and the thoracic tissues of dogs. The input signal of the model is the phonocardiogram recorded within the left ventricle, while the output signal is the phonocardiogram recorded on the chest wall over the apex of the heart. Changes in the transmission path characteritics are modeled by varying the frequency response of an equivalent acoustic system throughout the cardiac cycle. Experimental measurements in dogs were used to determine the general characteristics of left ventricular and apical first and second heart sound spectra, and from these, the transfer and coherence functions of the heart-thorax acoustic system. Results show that the transfer function of the heart-thorax acoustic system changes during the cardiac cycle. For the seven animals studied, it would appear that the contribution of left ventricular first and second sounds to the apical phonocardiogram is significant for frequencies below 70 Hz and negligible for frequencies above 250 Hz. In addition, it is shown that 80 percent of the power of sounds recorded on the chest wall arises from linear transmission of sounds recorded within the left ventricle through the heart-thorax acoustic system. The other 20 percent is due to thoracic noise, noncoherent cardiac contributions, and nonlinearity of the acoustic system.     

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.     

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.     

Information extraction from sound for medical telemonitoring.

Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patient's comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a data fusion process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance.     

Adaptive reduction of heart sounds from lung sounds usingfourth-order statistics

When recording lung sounds, an incessant noise source occurs due to heart sounds. This noise source severely contaminates the breath sound signal and interferes in the analysis of lung sounds. In this paper, an adaptive heart-noise reduction method, based on fourth-order statistics (FOS) of the recorded signal, without requiring recorded “noise-only” reference signal, is presented. This algorithm uses adaptive filtering to preserve the entire spectrum. Furthermore, the proposed filter is independent of Gaussian uncorrelated noise and insensitive to the step-size parameter. It converges fast with small excess errors and, due to the narrowband nature of heart noise (HN), it requires a very small number of taps. Results from experiments with healthy subjects indicate a local HN reduction equal to or greater than 90%     

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。      

Spectral composition of heart sounds before and after mechanicalheart valve implantation using a modified forward-backward Prony'smethod

This paper investigates the impact of the lung-thorax and heart-valve system on the overall spectral composition of the externally recorded heart sounds. The study concentrates on the case of the first and the second heart sounds for normal patients and patients before and after implantation of a mechanical valve in the mitral or aortic position. The analysis is performed using a modified forward-backward overdetermined Prony's method (MFBPM) which uses a forward-backward mean filter and a modified procedure for estimating the position of the signal poles. In terms of the normalized cross-correlation coefficient, this method has an average modeling accuracy of 99.62% for representing the first and second heart sounds and an average least square time-domain error of 0.43%. Results obtained from 40 subjects show that the condition of the native mitral or aortic valve affects mostly the distribution of the amplitudes of the spectral components, whereas the number of the spectral components or their respective relative energy remains more or less unchanged. It has been found that the amplitudes of frequency components in the range 120-250 Hz are more affected by abnormalities of native mitral valves. Furthermore, in the case of the second heart sound the region 250-400 Hz has been found to be more affected by abnormalities in the aortic valve. It has also been found that the mechanical prosthetic heart valve affects mostly the spectrum beyond 400 Hz. A clear difference has been observed in the frequency spectrum above 400 Hz between both normally and abnormally functioning native valves and normally functioning mechanical valves. Preliminary results in some malfunctioning cases of mechanical prosthesis suggest that spectral components beyond 400 Hz can be used to monitor the condition of these prostheses     

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。     

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     

Autoregressive modeling of lung sounds: characterization of sourceand transmission

In this communication, we discuss the application of autoregressive modeling to lung sounds analysis. The lung sounds source in the airway is modeled as a white noise source, consisting of one or a combination of the following sources: random white noise sequence, periodic train of impulses, and impulsive bursts of energy. The acoustic transmission through the lung parenchyma and chest wall is modeled as an all-pole filter. Using this method, the source and transmission characteristics of lung sounds are estimated separately, based on the lung sounds at the chest wall. To illustrate the potential validity of the model, lung sound segments in known disease conditions were selected from teaching tapes and the source and transmission characteristics were estimated by applying the model. The estimated characteristics were found to be consistent with current knowledge of the generation and transmission of lung sounds in the known conditions.     

Detection of cough signals in continuous audio recordings using hidden Markov models

Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient's chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording's average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.     

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.     

心脏量化听诊的新方法研究

本文从时频两域研究心脏量化听诊的新方法,实现心脏的诊断从定性到定量转变,即从时、频两域分别提取蕴含着丰富临床评价信息的心音特征波形,并依据所设定的阈值计算特征诊断参数,这些特征参数配以易于理解的图解和数字化参数来快速准确地判别正常心音与各种心脏杂音,该技术的实现不仅有利于缓解医生长时间听诊造成的听力疲劳,也使得其有可能成为一种大规模的人群心脏状况普查方法。     

The effect of anthropometric variations on acoustical flow estimation: proposing a novel approach for flow estimation without the need for individual calibration

Tracheal sound average power is directly related to the breathing flow rate and recently it has attracted considerable attention for acoustical flow estimation. However, the flow-sound relationship is highly variable among people and it also changes for the same person at different flow rates. Hence, a robust model capable of estimating flow from tracheal sounds at different flow rates in a large group of individuals does not exist. In this paper, a model is proposed to estimate respiratory flow from tracheal sounds. The proposed model eliminates the dependence of the previous methods on calibrating the model for every individual and at different flow rates. To validate the model, it was applied to the respiratory sound and flow data of 93 healthy individuals. We investigated the statistical correlation between the model parameters and anthropometric features of the subjects. The results have shown that gender, height, and smoking are the most significant factors that affect the model parameters. Hence, we grouped nonsmoker subjects into four groups based on their gender and height. The average of model parameters in each group was defined as the group-calibrated model parameters. These models were applied to estimate flow from data of subjects within the same group and in the other groups. The results show that flow estimation error based on the group-calibrated model is less than 10%. The low estimation errors confirm the possibility of defining a general flow estimation model for subjects with similar anthropometric features with no need for calibrating the model parameters for every individual. This technique simplifies the acoustical flow estimation in general applications including sleep studies and patients' screening in health care facilities.     

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。      

基于特征融合的海洋哺乳动物声音识别

为了提高海洋哺乳动物声音识别算法的识别率和鲁棒性,提出了一种将梅尔倒谱系数MFCC、线性倒谱系数LFCC和时域特征融合作为特征参数进行声音识别的方法。该方法通过融合不同倒谱系数以增强对不同频段的表征能力,通过融合时域特征来更全面地描述声音信息。声音样本通过基于海洋环境下的预处理、特征提取与融合后,用支持向量机进行分类识别。相对于传统算法只针对一种或几种哺乳动物进行识别,该方法在包含61种海洋哺乳动物声音的样本库中进行测试。测试结果显示该算法较传统的梅尔倒谱系数在识别率上提升了5.5%,且在海洋低信噪比环境下有更好的识别表现。     

Time-frequency transforms: a new approach to first heart soundfrequency dynamics

This study employed a new analytical tool, the Binomial joint time-frequency transform, to test the hypothesis that first heart sound frequency rises during the isovolumic contraction period. Cardiac vibrations were recorded from eight open chest dogs using an ultralight accelerometer cemented directly to the epicardium of the anterior left ventricle. The frequency response of the recording system was flat +/- 3 dB from 0.1 to 400 Hz. Three characteristic time-frequency spectral patterns were evident in the animals investigated: 1) A frequency component that rose from approximately 40-140 Hz in a 30-50 ms interval immediately following the ECG R-wave. 2) A slowly varying or static frequency of 60-100 Hz beginning midway through the isovolumic contraction period. 3) Broad-band peaks occurring at the time of the Ia and Ib high frequency components. The presence of rapid frequency dynamics limits the usefulness of stationary analysis techniques for the first heart sound. The Binomial transform provided much better resolution than the spectrograph or spectrogram, the two most common non-stationary signal analysis techniques. By revealing the onset and dynamics of first heart sound frequencies, time-frequency transforms may allow mechanical assessment of individual cardiac structures.