Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach

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dc.authorid0000-0002-4093-1059en_US
dc.contributor.authorÖzcan, Ahmet Remzi
dc.contributor.authorErturk, Sarp
dc.date.accessioned2021-03-20T20:12:26Z
dc.date.available2021-03-20T20:12:26Z
dc.date.issued2019
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractEpileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlation in the features obtained from multichannel EEG signals. Spectral band power, statistical moment and Hjorth parameters are used to reveal the frequency and time domain features of the EEG signals. The features are given as input to a convolutional neural network (CNN) by transforming into a sequence of multi-color images according to the topology of the EEG channels. The multi-frame 3D CNN model is proposed to evaluate the temporal and spatial correlation in training data collectively. The proposed 3D CNN model achieves a sensitivity of 85.7%, a false prediction rate of 0.096/h, and a proportion of time-in-warning of 10.5%, in the tests performed with 16 patients from the CHB-MIT scalp EEG dataset. The results show that the superiority of the proposed method to a Poisson based random predictor was statistically significant for 93.7% of the patients, at significance level of 0.05. Our experiments with various timing constraints show that epileptic stage lengths are an important factor affecting seizure performance. We present a subject-specific seizure prediction method that is robust for unbalanced data and can be generalized to any scalp EEG dataset without the need for subject-specific engineering.en_US
dc.identifier.doi10.1109/TNSRE.2019.2943707en_US
dc.identifier.endpage2293en_US
dc.identifier.issn1534-4320
dc.identifier.issn1558-0210
dc.identifier.issue11en_US
dc.identifier.pmid31562096en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2284en_US
dc.identifier.urihttp://doi.org/10.1109/TNSRE.2019.2943707
dc.identifier.urihttps://hdl.handle.net/20.500.12885/555
dc.identifier.volume27en_US
dc.identifier.wosWOS:000497685300005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorÖzcan, Ahmet Remzi
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions On Neural Systems And Rehabilitation Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectepilepsyen_US
dc.subjectepileptic stage lengthen_US
dc.subjectscalp EEGen_US
dc.subjectseizure predictionen_US
dc.subjectsubject-specific modellingen_US
dc.subjectunbalanced classificationen_US
dc.titleSeizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approachen_US
dc.typeArticleen_US

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