Intelligent Diagnosis System

Auscultation of heart murmurs can detect various types of heart problems; however, this process is prone to human error because it involves a clinician evaluating and categorizing the heart sound via stethoscope. By implementing Soft Computing techniques to analyze the sound received from the stethoscope and classify the heart pathology, this research project introduces the intelligent diagnosis system that is not based on subjective evaluations. The sound signal from the stethoscope is transformed into a spectrogram (an image that shows the time-based analysis of the frequency components) formed by taking the Fourier transform over a small sliding window in time. The magnitudes of the resulting Fourier transforms are mapped to a color function in a density plot. Color Reduction and Color Feature extraction methods are applied to convert the density plot to a manageable image characteristic and reduce the colors of the density plot from sixteen million to eight: red, green, blue, cyan, magenta, yellow, white, and black. The density plot represented by the eight- color vector is then matched with a database to identify a possible pathology.