Fundamentals of speech recognition
Fundamentals of speech recognition
Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Detection of heart valve diseases by using fuzzy discrete hidden Markov model
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Heart sound classification using wavelet transform and incremental self-organizing map
Digital Signal Processing
Diagnosis of valvular heart disease through neural networks ensembles
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Support Vectors Machine-based identification of heart valve diseases using heart sounds
Computer Methods and Programs in Biomedicine
Feature determination for heart sounds based on divergence analysis
Digital Signal Processing
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Brain computer interface control via functional connectivity dynamics
Pattern Recognition
Accuracy and robustness of decision making techniques in condition based maintenance
Journal of Intelligent Manufacturing
Noninvasive detection of mechanical prosthetic heart valve disorder
Computers in Biology and Medicine
Research and application of heart sound alignment and descriptor
Computers in Biology and Medicine
Classification of mechanical prosthetic heart valve sounds
International Journal of Computational Science and Engineering
2D and 3D palmprint information, PCA and HMM for an improved person recognition performance
Integrated Computer-Aided Engineering
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In this study, a biomedical diagnosis system for pattern recognition with normal and abnormal classes has been developed. First, feature extraction processing was made by using the Doppler Ultrasound. During feature extraction stage, Wavelet transforms and short-time Fourier transform were used. As next step, wavelet entropy were applied to these features. In the classification stage, hidden Markov model (HMM) was used. To compute the correct classification rate of proposed HMM classifier, it was compared to ANN by using a data set containing 215 samples. In our experiments, specificity rate and sensitivity rates of proposed HMM classifier system with fuzzy C means (FCM)/K-means algorithms were found as 92% and 97.26% respectively. The present study shows that proper selection of the HMMs initial parameter values according to FCM/K-means algorithms improves the recognition rate of the proposed system which was also compared to our previous study named ANN.