The nature of statistical learning theory
The nature of statistical learning theory
Wavelet applications in medicine
IEEE Spectrum
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Swarm intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A neural network model with bounded-weights for pattern classification
Computers and Operations Research
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Cluster and Classification Techniques for the Biosciences
Cluster and Classification Techniques for the Biosciences
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
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Computational Intelligence in Biomedical Engineering
Computational Intelligence in Biomedical Engineering
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
Classification of EMG signals using combined features and soft computing techniques
Applied Soft Computing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
Ensemble classification of colon biopsy images based on information rich hybrid features
Computers in Biology and Medicine
Muscle activity detection in electromyograms recorded during periodic movements
Computers in Biology and Medicine
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Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.