Separation of heart sounds and heart murmurs by Hilbert transform envelogram.

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.

Algorithm for detection of the internal components of the heart sounds and their split using a Hilbert transform.

Valvular heart disease is a serious heart condition that is difficult to diagnose in ambulatory settings. Heart sounds is one of the most relevant diagnosis signals in this context. The time interval between the two internal components of the two heart sounds in the medical field known as 'split' was considered by many researchers and one study is described as the key medical diagnosis by many clinicians. Compared to the energy envelope Shannon Hilbert envelope is greater awareness of the internal components of the first and second heart sound. The morphology of this envelope will allow one to apply the necessary tests for the temporal localization of the internal components of the two heart sounds. According to the results obtained, the Hilbert envelope is an approach and representation taking into account the physiological attenuation and giving a good separation.

Analysis of the pathological severity degree of aortic stenosis (AS) and mitral stenosis (MS) using the discrete wavelet transform (DWT).

The heart is the principal organ that circulates blood. In normal conditions it produces four sounds for each cardiac cycle. However, most often only two sounds appear essential: S1 and S2. Two other sounds: S3 and S4, with lower amplitude than S1 or S2, appear occasionally in the cardiac cycle by the effect of disease or age. The presence of abnormal sounds in one cardiac cycle provide valuable information on various diseases. The aortic stenosis (AS), as being a valvular pathology, is characterized by a systolic murmur due to a narrowing of the aortic valve. The mitral stenosis (MS) is characterized by a diastolic murmur due to a reduction in the mitral valve. Early screening of these diseases is necessary; it's done by a simple technique known as: phonocardiography. Analysis of phonocardiograms signals using signal processing techniques can provide for clinicians useful information considered as a platform for significant decisions in their medical diagnosis. In this work two types of diseases were studied: aortic stenosis (AS) and mitral stenosis (MS). Each one presents six different cases. The application of the discrete wavelet transform (DWT) to analyse pathological severity of the (AS and MS was presented. Then, the calculation of various parameters was performed for each patient. This study examines the possibility of using the DWT in the analysis of pathological severity of AS and MS.

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.

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.

ECG signal processing using multiresolution analysis.

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity=99.88% and predictivity=99.89%), and a total detection failure of 0.24%.

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.

Computerized heart sounds analysis.

This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency (TF) PCG signal characteristics and consequently aid diagnosis. Similarly, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties. The studied techniques (FT, STFT, WD, CWT, DWT and PWT) of analysis can thus be regarded as complementary in the TF analysis of the PCG signal; each will relate to a part distinct from the analysis in question.

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.

Analysis and study of the variation of splitting in the second heartbeat sound of wavelet transform.

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 the 'split'. A technique based on discrete wavelet transform (DWT) and continuous wavelet transform (CWT) is developed in this paper to measure the split. To quantify splitting, two components in S2 (i.e. A2 and P2) are identified, and the delay between the two components can be estimated. One normal case and three pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect) are considered in this study. The split is measured for each S2 sound of the considered signals. The split normally varies in duration over the cardiac cycle. In certain pathologies such as ASD (atrial septal defect) or PS (pulmonary stenosis), the split becomes fixed over the cardiac cycle. The main part of this paper consists of the identification and measurement of the S2 split. The study confirms the notion of 'variable splitting' for normal phonocardiogram and 'fixed splitting' for ASD and PS cases. This paper relates also to the establishment of statistical parameters to make a distinction between normal and pathological cases of phonocardiogram signals.

Time-frequency analysis of the second cardiac sound in phonocardiogram signals.

The paper is concerned with the analysis of the phonocardiogram signals (PCG) in the time-frequency domain. Three techniques are studied and evaluated in PCG signal analysis. These are the short time Fourier transform (STFT), the Wigner distribution function (WD) and the continuous wavelet transforms (CWTs). The analysis is first carried out on the second cardiac sound (S2) in order to show the aptitude of each method in distinguishing the internal components of this sound. The results we obtain show that the STFT cannot detect the two internal components of S2 (A2 and P2, respectively, the aortic and pulmonary components). The WD can provide time-frequency characteristics of S2, but with insufficient diagnostic information: the two components are not accurately detected and appear to be only one component. It is found that the CWT (it can also provide the time-frequency characteristic of S2) is capable of detecting its two components, A2 and P2, allowing therefore the measurement of the delay between them. This delay, called the split, is very important in the diagnosis of many pathological cases, as it is emphasized in the results we obtain by applying the CWT on different pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect).

Analysis of the second heart sound using continuous wavelet transform.

This paper is concerned with a synthesis study of Continuous Wavelet Transform (CWT) in analysing the second heart sound of the phonocardiogram (PCG). The second heart sound S2 consists of two major components (A2 and P2) with a time delay between them which is very important for a diagnosis. It is shown that CWT provides enough features of these components of time, frequency and time delay to aid diagnosis.