Artificial neural networks in computer-assisted classification of heart sounds in patients with porcine bioprosthetic valves.
The paper describes the design, training and testing of a three-layer feed-forward back-propagation neural network for the classification of bioprosthetic valve closure sounds. Forty-seven patients with a porcine bioprosthetic valve inserted in the aortic position were involved in the study. Twenty-four of them had a normal bioprosthetic valve, and the other 23 had a degenerated valve. Five features extracted from the Fourier spectra and 12 linear predictive coding (LPC) coefficients of the sounds were used separately as the input of two neural-network classifiers. The performance of the classifiers was tested using the leave-one-out method. Results show that correct classifications were 85 per cent using the spectral features, and 89 per cent using the LPC coefficients. The study confirms the potential of artificial networks for the classification of bioprosthetic valve closure sounds. Clinical use of this method, however, still requires further investigation.
Guo
Z
Z
Laboratory of Biomedical Engineering, Clinical Research Institute of Montreal, Quebec, Canada.
Durand
L G
LG
Lee
H C
HC
Allard
L
L
Grenier
M C
MC
Stein
P D
PD
D001021:Aortic Valve; D001705:Bioprosthesis; D006347:Heart Sounds; D006350:Heart Valve Prosthesis; D006801:Humans; D008433:Mathematics; D016571:Neural Networks, Computer; D010701:Phonocardiography; D011475:Prosthesis Failure