A quantitative classification of essential and Parkinson's tremor using wavelet transform and artificial neural network on sEMG and accelerometer signals.
Santosh Kumar NandaWen-Yen LinMing-Yih LeeRou-Shayn ChenPublished in: ICNSC (2015)
Keyphrases
- wavelet transform
- artificial neural networks
- power quality disturbance
- machine learning
- pattern classification
- pattern recognition
- support vector
- feature extraction
- image classification
- multiscale
- feature space
- multiresolution
- eeg signals
- genetic algorithm
- neural network
- acoustic signals
- classification algorithm
- wavelet coefficients
- classification accuracy
- supervised learning
- signal processing
- image processing
- face recognition
- feature vectors
- wavelet analysis
- learning rules
- input variables
- support vector machine
- image coding
- genetic algorithm ga
- class labels
- support vector machine svm
- model selection
- text classification
- denoising