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This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized 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 nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. 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 intracardiac 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, which separates the heart signal (or the phonocardiogram (PCG) signal), is based on the normalized average Shannon energy of the PCG signal. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

This paper presents a theoretical foundation for the newly developed methodology that enables the prediction of blood pressures based on the heart sounds measured directly on the chest of a patient. The key to this methodology is the separation of heart sounds into first heart sound and second heart sound, from which components attributable to four heart valves, i.e.: mitral; tricuspid; aortic; and pulmonary valve-closure sounds are separated. Since human physiology and anatomy can vary among people and are unknown a priori, such separation is called blind source separation. Moreover, the sources locations, their surroundings and boundary conditions are unspecified. Consequently, it is not possible to obtain an exact separation of signals. To circumvent this difficulty, we extend the point source separation method in this paper to an inhomogeneous fluid medium, and further combine it with iteration schemes to search for approximate source locations and signal propagation speed. Once these are accomplished, the signals emitted from individual sources are separated by deconvoluting mixed signals with respect to the identified sources. Both numerical simulation example and experiment have demonstrated that this approach can provide satisfactory source separation results.

This paper presents the analysis and comparisons of the short time Fourier transform (STFT) and the continuous wavelet transform techniques (CWT) to the four sounds analysis (S1, S2, S3 and S4). It is found that the spectrogram short-time Fourier transform (STFT), cannot perfectly detect the internals components of these sounds that the continuous wavelet transform. However, the short time Fourier transform can provide correctly the extent of time and frequency of these four sounds. Thus, the STFT and the CWT techniques provide more features and characteristics of the sounds that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

Although auscultation of the heart remains central in the detection of abnormal heart sounds and murmurs that are frequently indicative of serious cardiac pathology, the application of electronic methods to this end remains rarely used in daily clinical practice. In this report we provide a series of examples showing how the phonocardiogram can be analyzed quantitatively using color spectrographic techniques and discuss how such methods may be of future value for noninvasive cardiac monitoring. Using a sound recording system, we recorded the heart sounds and data acquisition using the program Gold Wave (http://www.goldwave.com) to collect data from a high-speed USB interface. Sample color spectrogram analysis of the obtained signals was performed using custom software written in MATLAB. Data was collected from a number of infants and adults with cardiac structural disease as well as from some normal individuals. Our data is presented as a series of 13 cases. We expect the application of spectrographic techniques to phonocardiography to grow substantially as ongoing research and clinical experience demonstrates its utility in various settings. Our evaluation of a simple, low-cost phonocardiographic recording and analysis system to assist in determining the characteristic features of heart murmurs shows promise in helping distinguish normal heart sounds, innocent systolic murmurs and pathological murmurs in children. It expected to be useful in other clinical settings as well.

Heart sound (HS) analysis or auscultation is a standout amongst the most simple, non-invasive and costless methods used to evaluate heart health and is one of the basic and foremost routine of a doctor while reviewing a patient. Detecting cardiac abnormality by auscultation demands a physician’s experience and even then there is a high scope of committing error. In this paper, a low cost electronic stethoscope is built to acquire HS in a novel manner by taking one from each ventricular and auricular area and superimposed, to get a resultant signal of both distinct lub-dub sound. Then, a light, fast and low computation speed beat track method followed by wavelet reconstruction is presented for correct detection of S1 and S2. It is done without ECG reference, and can be used satisfactorily on both normal and pathological HSs. Moreover, heartbeats can be identified in both de-noised and noised environment as it is independent of external disturbances. Significant features are extracted from the resultant HSs with detected S1 and S2 and feed-forward back propagation method. It is used to classify the HS nature into normal and pathological. This algorithm has been implemented on 24 pairs of HSs, extracted from 24 patients of 15 pathological and nine normal subjects and the classification yields a result of 91.7% accuracy with 81.8% sensitivity. The overall performance suggests a good performance to cost ratio. This system can be used as first diagnosis tool by the medical professionals.