Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
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
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases
Pattern Recognition Letters
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Wavelet-based denoising using subband dependent threshold for ECG signals
Digital Signal Processing
Feature determination for heart sounds based on divergence analysis
Digital Signal Processing
Optimal selection of wavelet basis function applied to ECG signal denoising
Digital Signal Processing
Multivariate Student-t self-organizing maps
Neural Networks
A geometric approach to the linear modelling
CSS'11 Proceedings of the 5th WSEAS international conference on Circuits, systems and signals
Optimized orthonormal wavelet filters with improved frequency separation
Digital Signal Processing
Computers in Biology and Medicine
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Determination of heart condition by heart auscultation is a difficult task and requires special training of medical staff. Computerized techniques suggest objective and more accurate results in a fast and easy manner. Hence, in this study it is aimed to perform computer-aided heart sound analysis to give support to medical doctors in decision making. In this study, a novel method is presented for the classification of heart sounds (HSs). Discrete wavelet transform is applied to windowed one cycle of HS. Wavelet transform is used both for the segmentation of S1-S2 sounds and determination of the features. Based on the third, fourth and the fifth decomposition-level detail coefficients, the timings of S1-S2 sounds are determined by an adaptive peak-detector. For the feature extraction, powers of detail coefficients in all five sub-bands are utilized. In the classification stage, Kohonen's SOM network and an incremental self-organizing map (ISOM) are examined comparatively. In order to increase the performance of heart sound classification, an incremental neural network is proposed in this study. It is observed that ISOM successfully classifies the HSs even in noisy environment.