Analysis of phonocardiogram signals using wavelet transform

Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the heart. They generally consist of two kinds of acoustic vibrations: heart sounds and heart murmurs. Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Heart auscultation has been recognized for a long time as an important tool for the diagnosis of heart disease, although its accuracy is still insufficient to diagnose some heart diseases. It does not enable the analyst to obtain both qualitative and quantitative characteristics of the PCG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse PCG signals using wavelet transform. This analysis is based on an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs using the PCG signal as the only source. The segmentation algorithm, which separates the components of the heart signal, is based on denoising by wavelet transform (DWT). This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs. Thus, the analysis of various PCGs signals using wavelet transform can provide a wide range of statistical parameters related to the phonocardiogram signal.     

Separation of heart sounds and heart murmurs by Hilbert transform envelogram

Abstract

Heart sounds and murmurs provide crucial diagnosis information for several heart diseases such as natural or prosthetic valve dysfunction and heart failure. Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intra-cardiac phonocardiography, combined with modern digital processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. The segmentation algorithm is based on the detection of the envelope of the phonocardiogram signal by the Hilbert transform technique, which is used to extract a smooth envelogram which enable one to apply the tests necessary for temporal localization of heart sounds and heart murmurs.     

Filtering and classification of phonocardiogram signals using wavelet transform

Auscultation is a technique in which a stethoscope is used to listen to the sounds of the heart. Structural defects of the heart are often reflected in the sounds the heart produces, and auscultation provides clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as clinical tool, it is difficult to analyse heart sound signals in the time or frequency domain. Thus phonocardiogram (PCG), recording of heart sounds has many advantages over traditional auscultation, in that they may be replayed and analysed for time and frequency information. Using discrete wavelet transform, the signal is decomposed and reconstructed without significant loss of information in the signal content. The error of rebuilding can be considered as an important parameter in the classification of the pathological severity of the phonocardiogram signals. Variation of this parameter is very sensitive to the murmur importance in PCG signals.     

Filtering and classification of phonocardiogram signals using wavelet transform

Auscultation is a technique in which a stethoscope is used to listen to the sounds of the heart. Structural defects of the heart are often reflected in the sounds the heart produces, and auscultation provides clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as clinical tool, it is difficult to analyse heart sound signals in the time or frequency domain. Thus phonocardiogram (PCG), recording of heart sounds has many advantages over traditional auscultation, in that they may be replayed and analysed for time and frequency information. Using discrete wavelet transform, the signal is decomposed and reconstructed without significant loss of information in the signal content. The error of rebuilding can be considered as an important parameter in the classification of the pathological severity of the phonocardiogram signals. Variation of this parameter is very sensitive to the murmur importance in PCG signals.     

The effectiveness of the wavelet transforms method in the heart sounds analysis

Heart sounds can be used more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and non-stationary that they are very difficult to analyse in time or frequency domains. We have studied the extraction of features from heart sounds in the time-frequency domain for recognition of heart sounds through time-frequency analysis. The application of wavelet transform for the heart sounds is thus described. The performance of continuous wavelet transform, discrete wavelet transform and packet wavelet transform is discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify clinical usefulness of our extraction methods for recognition of heart sounds.     

A hybrid algorithm for heart sounds segmentation based on phonocardiogram

Abstract

For many years, heart function has been measured by the electrocardiogram (ECG) signal, while sounds produced in the heart can also contain information indicating normal or abnormal heart function. What has caused to restrict the use of the phonocardiography (PCG) signal was the lack of mastery of experts in the interpretation of these sounds, as well as its high potential for noise pollution. PCG is a non-invasive signal for monitoring physiological parameters of cardiac, which can make heart disease diagnostics more efficient. In recent years, attempts have been made to use PCG to detect heart disease independently without a need to match with the ECG. We propose a hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function for detecting heart sounds to distinguish first (S1) and second (S2) cardiac sounds by eliminating the effect of cardiac murmurs. In this article, 250 normal and 250 abnormal sound signals were examined. The overall positive predictivity of normal and abnormal S1 and S2 is 98.98%, 98.78, 98.78 and 98.37, respectively. Our results showed that the proposed method has a high potential for heart sounds determination, while maintains its simplicity and has a reasonable computational time.     

Analysis of the four heart sounds statistical study and spectro-temporal characteristics

Abstract

Heart auscultation has been recognised for a long time as an important tool for the diagnosis of heart disease; it is the most common and widely recommended method to screen for structural abnormalities of the cardiovascular system. Detecting relevant characteristics and forming a diagnosis based on the sounds heard through a stethoscope, however, is a skill that can take years to be acquired and refine. The efficiency and accuracy of diagnosis based on heart sound auscultation can be improved considerably by using digital signal processing techniques to analyse phonocardiographic (PCG) signals. The study of the functioning of the heart is very important for the diagnosis of different cardiac pathologies. The phonocardiogram signal (PCG) is the signal generated after conversion of the sound noises coming from the heart into an electrical signal, it groups together a set of four cardiac noises (S1, S2, S3, S4) which are in direct correlation with cardiac activity. The short-term Fourier Transform (STFT) is an analytical technique that describes the evolution of the time and frequency behaviour of these four heart sounds. A statistical study has been carried out in this direction in order to better highlight the characteristics of the PCG signal. A fairly high number of cycles (twenty) was used to further refine the expected results. The objective of this paper is to use a statistical analysis based on the results obtained by the use of The STFT technic this in order to find statistical parameters (mean, standard deviation, etc.) which can give us a clear vision of the electrophysiological behaviour of the phonocardiogram signal. This aspect has not been done so far and which however can give appreciable practical results.     

Phonocardiogram signal analysis: a review

Many disease of the heart cause changes in heart sounds and additional murmurs before other signs and symptoms appear. Hence, heart sound analysis by auscultation is the primary test conducted by physicians to assess the condition of the heart. Yet, heart sound analysis by auscultation as well as analysis of the phonocardiogram (PCG) signal have not gained widespread acceptance. This is due mainly to many controversies regarding the genesis of the sounds and the lack of quantitative techniques for reliable analysis of the signal features. The heart sound signal has much more information than can be assessed by the human ear or by visual inspection of the signal tracings on paper as currently practiced. Here, we review the nature of the heart sound signal and the various signal-processing techniques that have been applied to PCG analysis. Some new research directions are also outlined.     

Matrix decomposition based feature extraction for murmur classification

Heart murmurs often indicate heart valvular disorders. However, not all heart murmurs are organic. For example, musical murmurs detected in children are mostly innocent. Because of the challenges of mastering auscultation skills and reducing healthcare expenses, this study aims to discover new features for distinguishing innocent murmurs from organic murmurs, with the ultimate objective of designing an intelligent diagnostic system that could be used at home. Phonocardiographic signals that were recorded in an auscultation training CD were used for analysis. Instead of the discrete wavelet transform that has been used often in previous work, a continuous wavelet transform was applied on the heart sound data. The matrix that was derived from the continuous wavelet transform was then processed via singular value decomposition and QR decomposition, for feature extraction. Shannon entropy and the Gini index were adopted to generate features. To reduce the number of features that were extracted, the feature selection algorithm of sequential forward floating selection (SFFS) was utilized to select the most significant features, with the selection criterion being the maximization of the average accuracy from a 10-fold cross-validation of a classification algorithm called classification and regression trees (CART). An average sensitivity of 94%, a specificity of 83%, and a classification accuracy of 90% were achieved. These favorable results substantiate the effectiveness of the feature extraction methods based on the proposed matrix decomposition method.     

Automatic detection of sounds and murmurs in patients with lonescu-Shiley aortic bioprostheses

The problems encountered in the automatic detection of cardiac sounds and murmurs are numerous. The phonocardiogram (PCG) is a complex signal produced by deterministic events such as the opening and closing of the heart valves, and by random phenomena such as blood-flow turbulence. In addition, background noise and the dependence of the PCG on the recording sites render automatic detection a difficult task. In the paper we present an iterative automatic detection algorithm based on the a priori knowledge of spectral and temporal characteristics of the first and second heart sounds, the valve opening clicks, and the systolic and diastolic murmurs. The algorithm uses estimates of the PCG envelope and noise level to identify iteratively the position and duration of the significant acoustic events contained in the PCG. The results indicate that it is particularly effective in detecting the second heart sound and the aortic component of the second heart sound in patients with lonescu-Shiley aortic valve bioprostheses. It has also some potential for the detection of the first heart sound, the systolic murmur and the diastolic murmur.     

Development of a cardiac acoustic mapping system

The authors describe a cardiac acoustic mapping system designed to acquire, analyse, and display the amplitude distribution of a phonocardiogram (PCG) recorded from 22 sites on the thorax. A new PCG envelope detection approach, implemented by analogue circuits, enables simultaneous sampling of PCG envelopes from the 22 sites at a rate of 250 Hz/channel. A calibration procedure removes channel-to-channel gain variations, and a coherent averaging of each PCG envelope over several cardiac cycles improves the signal-to-noise ratio. Time congruent samples from the 22 averaged PCG envelopes are then interpolated in 2D to generate the isoamplitude maps. Preliminary results show that the acoustic maps can help locate the source of heart sounds and murmurs, and characterise their radiation and propagation patterns. Cardiac acoustic mapping opens up new avenues for the study of the propagation of heart sounds and murmurs through the heart-thorax acoustic system. This technique could contribute to improvements in the diagnosis of valvular dysfunctions.     

Automatic measure of the split in the second cardiac sound by using the wavelet transform technique

This paper is concerned with the identification and automatic measure of the split in the second heart sound (S2) of the phonocardiogram signal (PCGs) for normal or pathological case. The second heart sound S2 consists of two acoustic components A2 and P2, the former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as "split". A automatic technique based on the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT) is developed in this paper to measure the split of the second cardiac sound (S2) for the normal and pathological cases of the PCG signals. To quantify the splitting, the two components in S2 (i.e. A2 and P2) are identified and, the delay between the two components can be estimated. It is shown that the wavelet transform can provide best information and features of the split of S2 and the major components (A2 and P2) and consequently aid in medical diagnosis.     

Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders

Heart murmurs are pathological sounds produced by turbulent blood flow due to certain cardiac defects such as valves disorders. Detection of murmurs via auscultation is a task that depends on the proficiency of physician. There are many cases in which the accuracy of detection is questionable. The purpose of this study is development of a new mathematical model of systolic murmurs to extract their crucial features for identifying the heart diseases. A high resolution algorithm, multivariate matching pursuit, was used to model the murmurs by decomposing them into a series of parametric time–frequency atoms. Then, a novel model-based feature extraction method which uses the model parameters was performed to identify the cardiac sound signals. The proposed framework was applied to a database of 70 heart sound signals containing 35 normal and 35 abnormal samples. We achieved 92.5% accuracy in distinguishing subjects with valvular diseases using a MLP classifier, as compared to the matching pursuit-based features with an accuracy of 77.5%.     

基于小波变换的心音包络提取算法及应用

背景：心音信号包含了大量心脏瓣膜活动的生理信息,心音分析对诊断心脏疾病具有重要的临床意义。 目的：旨在通过心音的包络提取,分析心音信号的各种特征,进而判断心音中是否包含杂音,以改善传统听诊技术高度依赖医生经验、听诊范围受限的缺点。 方法：提出了一种采用小波变换来提取心音包络的方法,通过与采用希尔伯特-黄变换、数学形态学、平均香农能量等心音包络求解方法进行对比,证明这种方法具有算法简便、曲线光滑、特征点突出等优点。 结果与结论：将该方法用于临床真实心音的包络提取,利用支持向量机来训练所提取心音包络的面积和小波能量两个特征参数,判别心音信号是否明显包含杂音。选用35例心音数据对算法进行验证,结果表明该算法的准确率达到95%,具有很强的实用性。     

Phonocardiogram signal analysis: Techniques and performance comparison

This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the phonocardiogram (PCG) signals. A comparison between these three methods has shown the resolution differences between them. It is found that the spectrogram short-time Fourier transform (STFT), cannot detect the four components of the first sound of the PCG signal. Also, the two components of the second sound are inaccurately detected. The Wigner distribution can provide time-frequency characteristics of the PCG signal, but with insufficient diagnostic information: the four components of the first sound, SI, are not accurately detected and the two components of the second sound, S2, seem to be one component. It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary value component P2, of the second sound S2 of a normal PCG signal. These components are not detectable using the spectrogram or the Wigner distribution. However, the standard Fourier transform can display these two components in frequency but not the time delay between them. Furthermore, the wavelet transform provides more features and characteristics of the PCG signals that mill help physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.     

A review of signal processing techniques for heart sound analysis in clinical diagnosis

This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority.     

A review of signal processing techniques for heart sound analysis in clinical diagnosis

This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority.     

基于小波多分辨分析的第一第二心音提取

在心音信号的分析中,为了有针对性的分析第一心音(S1)、第二心音(S2)、第三心音(S3)、第四心音(S4),首先需要将它们从采集的心音信号中分离出来.本文提出一种方法,利用小波多分辨分析,提取第一心音及第二心音的同步信号,从而完成对第一心音和第二心音的实时分离.该方法区别于传统的依靠心电信号进行同步提取第一心音和第二心音的方法,避免了采集心音信号的同时需采集心电信号的麻烦,也提供了一种实现信号自同步的思路.用本方法对28例心音信号进行了仿真实验,1、S2均能被正确地分离出,表明该方法是可行的.     

Computer aided analysis of phonocardiogram

In the present paper analysis of phonocardiogram (PCG) records are presented. The analysis has been carried out in both time and frequency domains with the aim of detecting certain correlations between the time and frequency domain representations of PCG. The analysis is limited to first and second heart sounds (S1 and S2) only. In the time domain analysis the moving window averaging technique is used to determine the occurrence of S1 and S2, which helps in determination of cardiac interval and absolute and relative time duration of individual S1 and S2, as well as absolute and relative duration between them. In the frequency domain, fast Fourier transform (FFT) of the complete PCG record, and short time Fourier transform (STFT) and wavelet transform of individual heart sounds have been carried out. The frequency domain analysis gives an idea about the dominant frequency components in individual records and frequency spectrum of individual heart sounds. A comparative observation on both the analyses gives some correlation between time domain and frequency domain representations of PCG.     

Computer aided analysis of phonocardiogram

In the present paper analysis of phonocardiogram (PCG) records are presented. The analysis has been carried out in both time and frequency domains with the aim of detecting certain correlations between the time and frequency domain representations of PCG. The analysis is limited to first and second heart sounds (S1 and S2) only. In the time domain analysis the moving window averaging technique is used to determine the occurrence of S1 and S2, which helps in determination of cardiac interval and absolute and relative time duration of individual S1 and S2, as well as absolute and relative duration between them. In the frequency domain, fast Fourier transform (FFT) of the complete PCG record, and short time Fourier transform (STFT) and wavelet transform of individual heart sounds have been carried out. The frequency domain analysis gives an idea about the dominant frequency components in individual records and frequency spectrum of individual heart sounds. A comparative observation on both the analyses gives some correlation between time domain and frequency domain representations of PCG.