Authors:
Yuedong Song
1
;
Sarita Azad
1
and
Pietro Lio
2
Affiliations:
1
University of Cambridge, United Kingdom
;
2
Univeristy of Cambridge, United Kingdom
Keyword(s):
Epileptic seizure detection, Electroencephalogram (EEG), Discrete Wavelet Transform, Extreme Learning Machine (ELM).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this paper, we investigate the potential of discrete wavelet transform (DWT), together with a recentlydeveloped machine learning algorithm referred to as Extreme Learning Machine (ELM), to the task of classifying EEG signals and detecting epileptic seizures. EEG signals are decomposed into frequency sub-bands using DWT, and then these sub-bands are passed to an ELM classifier. A comparative study on system performance is conducted between ELM and back-propagation neural networks (BPNN). Results show that the
ELM classifier not only achieves better classification accuracy, but also needs much less learning time compared to the BPNN classifier. It is also found that the length of the EEG segment used affects the prediction performance of classifiers.