Abstract
This study examines the utility of neural networks for 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 compIex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on linear prediction coefficients by using the Autoregressive (AR) after Adaptive Line Enhancement (ALE) method. In order to further explore the extraction of the useful information regarding the complex diastolic heart sounds assoicated with coronary artery disease, the analysis of the diastolic heart sounds was approached using neural networks since neural networks are potentially capable of partitioning the signal space into arbitrarily complex decision regions.
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