Title :
Noninvasive detection of coronary artery disease
Author :
Akay, Yasemin M. ; Akay, Metin ; Welkowitz, Walter ; Kostis, John
Author_Institution :
Dept. of Biomed. Eng., Rutgers Univ., Piscataway, NJ, USA
Abstract :
Recently, the authors focused on the application of the neural networks to improve the diagnostic capability of the acoustical approach. In order to improve the diagnostic ability for mis-diagnosed patients, the combination of the first four moments (mean, variance, skewness, kurtosis) of the extrema of the coefficients of wavelet transform applied to the diastolic heart sounds associated with coronary artery disease, as well as physical examination parameters, were used as the input pattern to the neural networks. The wavelet transform was chosen, since it is free from assumptions concerning the characteristics of the signal. Finally, using their nonlinear and multilayered architecture, fuzzy neural networks were applied to the diastolic heart sounds produced by coronary stenoses in order to capture fully all relevant information related to the patients´ disease states.<>
Keywords :
bioacoustics; cardiology; medical diagnostic computing; medical signal processing; wavelet transforms; coefficients extrema moments; coronary artery disease; coronary stenoses; diagnostic capability improvement; diastolic heart sounds; fuzzy neural network; medical diagnostic technique; misdiagnosed patients; noninvasive detection; nonlinear multilayered architecture; patient disease state; physical examination parameters; signal characteristics; Arteries; Blood flow; Cardiac disease; Cardiovascular diseases; Coronary arteriosclerosis; Fuzzy neural networks; Heart; Neural networks; Wavelet analysis; Wavelet transforms;
Journal_Title :
Engineering in Medicine and Biology Magazine, IEEE
