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Heart sound analysis has been a topic of investigation for several years. Since heart sounds directly encode the mechanical activity of the heart, they enable the assessment and follow-up of several types of heart disorders in pre-symptomatic states. Murmurs are the most common abnormality signature in many heart disorders. This paper introduces an algorithm for heart murmur identification. In the presence of murmurs, heart sounds exhibit chaotic behavior. In the proposed method this is assessed based upon the nonlinear dynamics of the signal. In order to segment murmurs from other heart sound components, the signal is transformed into a phase space that is later reconstructed using the embedded matrix. Based on the phase space, the complexity and the strength of the signal are computed. These features are the basis for sound component boundary location. The method has been tested with a database of heart sounds that include diverse heart lesions and heart murmurs. The algorithms achieved 91.09% sensitivity and 95.25% specificity.

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As image enhancement is the main issue for biomedical image diagnosis, the aim of this paper is to present a method to enhance the biomedical images. In this paper, a combination of wavelets is used for the same. In the method after applying SIFT(Scale Invariant Feature Transforms) algorithm on the image the first wavelet D'Mayer is applied, then image is extracted and the second wavelet Coieflet is applied on the image. The results of the proposed method have been compared with other wavelets on the basis of different metrics like PSNR (Peak signal to noise ratio) and Beta coefficient and it has been found that the proposed method provides better results than the other methods.