ABSTRACT
This chapter examines the utility of wavelet-based neural networks for detecting coronary artery disease noninvasively by using the clinical examination variables and extracting useful information from the diastolic heart sounds associated with coronary occlusions.
It has been widely reported that coronary stenoses produce sounds due to the turbulent blood flow in these vessels. These complex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on extrema representation of the fast wavelet transform coefficients. In addition, some physical exam variables such as sex, age, body weight, smoking condition, diastolic pressure, systolic pressure, and derivation from them were included in the feature vector. This feature vector was used as the input pattern to the neural network.
