The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Support Vector Machines for Pattern Classification
Support Vector Machines for Pattern Classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature-based Type Identification of File Fragments
Security and Communication Networks
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Computers in Biology and Medicine
International Journal of Knowledge Discovery in Bioinformatics
Expert Systems with Applications: An International Journal
International Journal of Mobile Learning and Organisation
Hi-index | 12.06 |
In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.