Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications
Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
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
Computerized heart sounds analysis
Computers in Biology and Medicine
IEEE Transactions on Signal Processing
Towards designing modular recurrent neural networks in learning protein secondary structures
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Research and application of heart sound alignment and descriptor
Computers in Biology and Medicine
Classifying heart sounds using multiresolution time series motifs: an exploratory study
Proceedings of the International C* Conference on Computer Science and Software Engineering
Multi-level basis selection of wavelet packet decomposition tree for heart sound classification
Computers in Biology and Medicine
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Cardiac auscultatory proficiency of physicians is crucial for accurate diagnosis of many heart diseases. Plenty of diverse abnormal heart sounds with identical main specifications and different details representing the ambient noise are indispensably needed to train, assess and improve the skills of medical students in recognizing and distinguishing the primary symptoms of the cardiac diseases. This paper proposes a versatile multiresolution wavelet-based algorithm to first extract the main statistical characteristics of three well-known heart valve disorders, namely the aortic insufficiency, the aortic stenosis, and the pulmonary stenosis sounds as well as the normal ones. An artificial neural network (ANN) and statistical classifier are then applied alternatively to choose proper exclusive features. Both classification approaches suggest using Daubechies wavelet filter with four vanishing moments within five decomposition levels for the most prominent distinction of the diseases. The proffered ANN is a multilayer perceptron structure with one hidden layer trained by a back-propagation algorithm (MLP-BP) and it elevates the percentage classification accuracy to 94.42. Ultimately, the corresponding main features are manipulated in wavelet domain so as to sequentially regenerate the individual counterparts of the underlying signals.