Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers
Joint Authors
Ozdemir, Nilufer
Yildirim, Esen
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The aim of this paper is to develop an automated system for epileptic seizure prediction from intracranial EEG signals based on Hilbert-Huang transform (HHT) and Bayesian classifiers.
Proposed system includes decomposition of the signals into intrinsic mode functions for obtaining features and use of Bayesian networks with correlation based feature selection for binary classification of preictal and interictal recordings.
The system was trained and tested on Freiburg EEG database.
58 hours of preictal data, 40-minute data blocks prior to each of 87 seizures collected from 21 patients, and 503.1 hours of interictal data were examined resulting in 96.55% sensitivity with 0.21 false alarms per hour, 13.896% average proportion of time spent in warning, and 33.21 minutes of average detection latency using 30-second EEG segments with 50% overlap and a simple postprocessing technique resulting in a decision (a seizure is expected/not expected) every 5 minutes.
High sensitivity and low false positive rate with reasonable detection latency show that HHT based features are acceptable for patient specific seizure prediction from intracranial EEG data.
Time spent for testing an EEG segment was 4.1451 seconds on average, which makes the system viable for use in real-time seizure control systems.
American Psychological Association (APA)
Ozdemir, Nilufer& Yildirim, Esen. 2014. Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1016825
Modern Language Association (MLA)
Ozdemir, Nilufer& Yildirim, Esen. Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1016825
American Medical Association (AMA)
Ozdemir, Nilufer& Yildirim, Esen. Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1016825
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1016825