Authors:
Jing Wang
1
and
Ping Guo
2
Affiliations:
1
Beijing Normal University, School of Foundational Education and Peking University Health Science Center, China
;
2
Beijing Normal University, China
Keyword(s):
Electroencephalogram (EEG) signals, Epilepsy seizures, Seizure detection, Overcomplete dictionary, Sparse representation, Bayesian decision rule.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Data Manipulation
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
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:
Epilepsy seizure detection in Electroencephalogram (EEG) is a major issue in the diagnosis of epilepsy and it can be considered as a classification problem. According to the particular property of EEG, a novel method based on sparse representation is proposed for epilepsy detection in this paper. Classification accuracy, robustness on noisy data and parameters (the size of dictionary and the number of features) of proposed method are tested and analysed on the public available data. The proposed method can obtain the highest classification accuracy among the discussed methods when the suitable parameters are set, and the proposed method based on sparse representations for classification is robust to noise. This is consistent with the theory that sparse representations can capture the inherent structure of signal. Furthermore, it is shown by experiments that the optimal selection of the parameters is critical to the performance of epilepsy detection.