Abstract
Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients. Cross-correlation coefficients are extracted every 2 seconds using a 4-second window with 50% overlap from focus electrodes identified by the epileptologist. Features are further processed by a second-order Kalman filter and then input to three different classifiers which include AdaBoost, radial basis function kernel support vector machine (RBF-SVM) and artificial neural network (ANN). The algorithm is tested on the long-term intra-cranial EEG (iEEG) database collected at the UMN epilepsy clinic. This database includes EEG recordings from 2 patients sampled from varying number of electrodes sampled at 2kHz. It is shown that the proposed algorithm achieves a high sensitivity and a low false positive rate.
Original language | English (US) |
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Title of host publication | Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 775-779 |
Number of pages | 5 |
ISBN (Electronic) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2016-February |
ISSN (Print) | 1058-6393 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.