Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers

Basit Öğe Kaydı
dc.contributor.authorOzdemir, Nilufer
dc.contributor.authorYildirim, Esen
dc.date.accessioned2024-09-18T21:06:27Z
dc.date.available2024-09-18T21:06:27Z
dc.date.issued2014
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThe 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.en_US
dc.description.sponsorshipTurkish Scientific and Technical Research Council (TUBITAK) [109E223]en_US
dc.description.sponsorshipThis work was supported by the Turkish Scientific and Technical Research Council (TUBITAK) under Project no. 109E223. The authors are very grateful to the Epilepsy Center of the University Hospital of Freiburg, Germany, for their consent to use the invasive EEG recordings in this work.en_US
dc.identifier.doi10.1155/2014/572082
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.scopus2-s2.0-84929273623en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1155/2014/572082
dc.identifier.urihttps://hdl.handle.net/20.500.12483/13613
dc.identifier.volume2014en_US
dc.identifier.wosWOS:000342932100001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEmpirical Mode Decompositionen_US
dc.subjectEpileptic Seizuresen_US
dc.subjectFrameworken_US
dc.titlePatient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiersen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Tam Metin / Full Text
Boyut:
722.79 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu