Model of Differentiation between Normal and Abnormal Heart Sounds in Using the Discrete Wavelet Transform

Today, modern technology has provided more powerful tools to evaluate the information related to heart sounds that traditional tools like stethoscope cannot achieve. One of the most common methods used for listening and tracking the heart sounds is to record them with special devices. The recorded heart sounds is known as PCG (phonocardiogram) signal. It is a particularly useful diagnosis tool since it contains different timings and relative intensities of heart beat sounds which are directly related to heart activity. The objective of this paper we develop a simple model for analysing the PCG signal in order to distinguish between normal and abnormal heart sounds. This analysis is carried out by using discrete wavelet transform. By using the discrete wavelet transform (DWT) the PCG signal is decomposed in to 7 stages. The average standard deviation of the detailed coefficients at each stage is calculated for each signal. The slopes of these curves for each case are obtained by plotting the average standard deviation of the detailed coefficients at each level detail. The analysis of these slopes shown that the discrimination between the normal signals from abnormal is possible