Wavelet applications in medicine
IEEE Spectrum
Signal Processing, Image Processing and Pattern Recognition
Signal Processing, Image Processing and Pattern Recognition
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases
Pattern Recognition Letters
Brain tumor classification based on long echo proton MRS signals
Artificial Intelligence in Medicine
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Prediction of flashover voltage of insulators using least squares support vector machines
Expert Systems with Applications: An International Journal
Advances in Engineering Software
A hybrid reasoning mechanism integrated evidence theory and set pair analysis in Swine-Vet
Expert Systems with Applications: An International Journal
Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier
Expert Systems with Applications: An International Journal
Model selection for least squares support vector regressions based on small-world strategy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
There has been a growing research interest in the use of intelligent methods in biomedical studies. This is the result of developments in the area of data analysis and classifying techniques. In this paper, an expert system based on least squares support vector machines (LS-SVM) for diagnosis of valvular heart disease (VHD) is presented. Wavelet packet decomposition (WPD) and fast-Fourier transform (FFT) methods are used for feature extracting from Doppler signals. LS-SVM is used in the classification stage. Threefold cross-validation method is used to evaluate the proposed expert system performance. The performances of the developed systems were evaluated in 105 samples that contain 39 normal and 66 abnormal subjects for mitral valve disease. The results showed that this system is effective to detect Doppler heart sounds. The average correct classification rate was about 96.13% for normal subjects and abnormal subjects.