Abstract
The paper applies artificial neural networks (ANNs) to the analysis of heart sound abnormalities through auscultation. Audio auscultation samples of 16 different coronary abnormalities were collected. Data pre-processing included down-sampling of the auscultated data and use of the fast Fourier transform (FFT) and the Levinson-Durbin autoregression algorithms for feature extraction and efficient data encoding. These data were used in the training of a multi-layer perceptron (MLP) and radial basis function (RBF) neural network to develop a classification mechanism capable of distinguishing between different heart sound abnormalities. The MLP and RBF networks attained classification accuracies of 84% and 88%, respectively. The application of ANNs to the analysis of respiratory auscultation and consequently the development of a combined cardio-respiratory analysis system using auscultated data could lead to faster and more efficient treatment.
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Folland, R., Hines, E.L., Boilot, P. et al. Classifying coronary dysfunction using neural networks through cardiovascular auscultation. Med. Biol. Eng. Comput. 40, 339–343 (2002). https://doi.org/10.1007/BF02344217
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DOI: https://doi.org/10.1007/BF02344217