Abstract
Epilepsy is a common neurological disorder involving spontaneous seizures. While electroencephalography (EEG) is a useful diagnostic approach of epilepsy that records the electrical activity of the brain, detection of epileptic seizures is still clinically difficult. This study proposes a segmental classification approach for the detection of epileptic seizure in EEG signals. Regularized least squares and smoothness priors methods are applied to minimize the nonstationary components in the signals. The optimal frequency band energy features are selected by using the sequential floating forward selectrion (SFFS) algorithm, with linear, quadratic and cubic discriminant function as classifiers. The results show that when the quadratic discriminant function is applied, the sensitivity and specificity of seizure detection reach a maximum of 98.1% and 95.6% respectively for discriminating health subjects against epileptic subjects in seizure period, and the overall classification rate is 97.2%.
Original language | English |
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Title of host publication | ICSP 2012 - 2012 11th International Conference on Signal Processing, Proceedings |
Pages | 1637-1640 |
Number of pages | 4 |
Volume | 3 |
DOIs | |
Publication status | Published - 1 Dec 2012 |
Event | 2012 11th International Conference on Signal Processing, ICSP 2012 - Beijing, China Duration: 21 Oct 2012 → 25 Oct 2012 |
Conference
Conference | 2012 11th International Conference on Signal Processing, ICSP 2012 |
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Country/Territory | China |
City | Beijing |
Period | 21/10/12 → 25/10/12 |
Keywords
- discriminant functions
- EEG
- epilepsy
- seizure detection
- sequential floating forward selection
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Science Applications