The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A time-varying AR modeling of heart wall vibration
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature selection for the SVM: An application to hypertension diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparison of envelope extraction algorithms for cardiac sound signal segmentation
Expert Systems with Applications: An International Journal
Intelligent target recognition based on wavelet packet neural network
Expert Systems with Applications: An International Journal
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier
Expert Systems with Applications: An International Journal
Automatic phonocardiograph signal analysis for detecting heart valve disorders
Expert Systems with Applications: An International Journal
Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram
Expert Systems with Applications: An International Journal
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
Computer Methods and Programs in Biomedicine
Risk prediction for postoperative morbidity of endovascular aneurysm repair using ensemble model
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Journal of Medical Systems
Multi-level basis selection of wavelet packet decomposition tree for heart sound classification
Computers in Biology and Medicine
International Journal of Mobile Learning and Organisation
| Hi-index | 12.06 |
In this study, the valvular heart disorder (VHD) detection method by the wavelet packet (WP) decomposition and the support vector machine (SVM) techniques are proposed. From considering the truth that the frequency ranges of the normal sound and VHDs are different from each other, the WP decomposition at level 8 is employed to split more elaborate frequency bandwidths of the heart sound signals. And then the WP energy (WPE) with the distribution information of energy throughout the whole frequency range of heart sound signals is calculated. Since the heart sound signals with the frequency range of 20-750Hz are preferred in this study, WPEs at the terminal nodes from (8,1) to (8,47) are selected and two parameters meanWPE and stdWPE as defined by the mean value and standard deviation of the position indices of the terminal nodes with over the weighting value (@z) of the maximum value of WPE are proposed as a feature. Furthermore, the SVM technique is employed as the identification tool to classify between the normal sound and VHDs. Finally, a case study on the normal sound, aortic and mitral VHDs is demonstrated to validate the usefulness and efficiency of the VHD detection using WP decomposition and SVM classifier. The experimental results of the proposed VHD detection method showed high performance like the specificity of over 96% and the sensitivity of 100% for both the training and testing data.