A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Filter design for cancellation of baseline-fluctuation in needle EMG recordings
Computer Methods and Programs in Biomedicine
Computers in Biology and Medicine
Hi-index | 0.00 |
This paper describes a new method for the classification of neuromuscular disorders based on the analysis of scalograms determined by the Symlet 4 wavelet technique. The approach involves isolating single motor unit action potentials (MUAPs), computing their scalograms, taking the maximum values of the scalograms in five selected scales, and averaging across MUAPs to give a single 5-dimensional feature vector per subject. After SVM analysis, the vector is reduced to a single decision parameter, called the Wavelet Index, allowing the subject to be assigned to one of three groups: myogenic, neurogenic or normal. The software implementation of the method described above created a tool supporting electromyographic (EMG) examinations. The method is characterized by a high probability for the accurate diagnosis of muscle state. The method produced 5 misclassifications out of 800 examined cases (total error of 0.6%).