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The Phonocardiogram signal is a combination of contraction and relaxation of atria and ventricles. Valve movements play an important role in the medical field by providing the clinician with diagnostic and prognostic information. The energy associated with the signal can be useful in quantification of various non-deterministic events such as cavitation in mechanical heart valve patients, mitral regurgitation, pulmonary stenosis and murmurs. As the PCG signals are complex and non-stationary, the energy calculated in time and frequency domain is difficult to analyze. This paper analyzes the importance of using wavelets in non-deterministic energy calculation using two versions of wavelet transforms: the Continuous Wavelet Transform (CWT) and Packet Wavelet Transform (PWT). The analysis is obtained by using large number of orthogonal and bi-orthogonal wavelets in finding the difference between the deterministic and total energy, which leads to the non-deterministic energy of the signal. The performance of CWT and PWT in non-deterministic energy calculation is evaluated. The results obtained are analyzed and discussed.

Knee osteoarthritis (OA) is a degenerative joint disease that occurs due to wear down of cartilage. Early diagnosis has a pivotal role in providing effective treatment and in attenuating further effects. This chapter aims to grade the severity of knee OA into three classes, namely absence of OA, mild OA, and severe OA, from radiographic images. Pre-processing steps include CLAHE and anisotropic diffusion for contrast enhancement and noise reduction, respectively. Niblack thresholding algorithm is used to segment the cartilage region. GLCM features like contrast, correlation, energy, homogeneity, and cartilage features such as area, medial, and lateral thickness are extracted from the segmented region. These features are fed to random forest classifier to assess the severity of OA. Performance of random forest classifier is compared with ANFIS and Naïve Bayes classifier. The classifiers are trained with 120 images and tested with 45 images. Experimental results show that random forest classifier achieves a higher accuracy of 88.8% compared to ANFIS and Naïve Bayes classifier.