The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Review: A new training method for support vector machines: Clustering k-NN support vector machines
Expert Systems with Applications: An International Journal
Detection of valvular heart disorders using wavelet packet decomposition and support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Computers in Biology and Medicine
Classification of mechanical prosthetic heart valve sounds
International Journal of Computational Science and Engineering
Hi-index | 12.05 |
Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set. In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error. The basic idea is to enlarge the separating boundary surface, such that the separability between the clusters is increased. The updated weight vector is used at the time of testing. The performance of the proposed systems is evaluated on 64 different recordings of heart sounds comprising of normal and five different pathological cases. It is found that the proposed technique classifies the heart sounds with higher recognition accuracy than competing techniques.