Abstract
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients from a single-channel intra-cranial electroencephaolograph (iEEG) recording. Instead of extracting features from the EEG signal, first the EEG signal is filtered by a prediction error filter (PEF) to compute a prediction error signal. A two-level wavelet decomposition of the prediction error signal leads to two detail signals and one approximate signal. Eight features are extracted every one second using a 2-second window with a 50% overlap. These features are input to two different types of classifiers: a linear support vector machine (SVM) classifier and an AdaBoost classifier. The algorithm is tested using the intra-cranial EEG (iEEG) from the Freiburg database. It is shown that the proposed algorithm can achieve a sensitivity of 95.0% and an average false positive rate (FPR) of 0.124 per hour, using the linear SVM classifier. The AdaBoost classifier achieves a sensitivity of 98.75% and an average FPR of 0.075 per hour. These results are obtained with leave-one-out cross-validation. In addition, for 13 out of 18 patients, the AdaBoost classifier requires only one feature, while it requires 4 features for the remaining 5 patients.
Original language | English (US) |
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Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4443-4446 |
Number of pages | 4 |
Volume | 2014 |
ISBN (Electronic) | 9781424479290 |
DOIs | |
State | Published - Nov 2 2014 |
Event | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014 |
Publication series
Name | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
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Other
Other | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
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Country/Territory | United States |
City | Chicago |
Period | 8/26/14 → 8/30/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
PubMed: MeSH publication types
- Journal Article