A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Automatic measure of the split in the second cardiac sound by using the wavelet transform technique
Computers in Biology and Medicine
Comparison of envelope extraction algorithms for cardiac sound signal segmentation
Expert Systems with Applications: An International Journal
Computerized heart sounds analysis
Computers in Biology and Medicine
A computer-aided MFCC-based HMM system for automatic auscultation
Computers in Biology and Medicine
Detection of valvular heart disorders using wavelet packet decomposition and support vector machine
Expert Systems with Applications: An International Journal
Noninvasive detection of mechanical prosthetic heart valve disorder
Computers in Biology and Medicine
A Multiclass Classification Tool Using Cloud Computing Architecture
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Computers in Biology and Medicine
Classification of mechanical prosthetic heart valve sounds
International Journal of Computational Science and Engineering
Hi-index | 12.05 |
Skilled cardiologists probe heart sounds by electronic stethoscope through human ears, but interpretations of heart sounds is a very special skill which is quite difficult to teach in a structured way. Because of this reason, automatic heart sound analysis in computer systems would be very helpful for medical staffs. This paper presents a complete heart sound analysis system covering from the segmentation of beat cycles to the final determination of heart conditions. The process of heart beat cycle segmentation includes autocorrelation for predicting the cycle time of a heart beat. The feature extraction pipeline includes stages of the short-time Fourier transform, the discrete cosine transform, and the adaptive feature selection. Many features are extracted, but only a few specific ones are selected for the classification of each hyperplane based on a systematic approach. The experiments are done by a public heart sound database released by Texas Heart Institute. A very promising recognition rate has been achieved.