Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최선아 | * |
dc.date.accessioned | 2020-12-14T16:30:08Z | - |
dc.date.available | 2020-12-14T16:30:08Z | - |
dc.date.issued | 2020 | * |
dc.identifier.issn | 1664-2295 | * |
dc.identifier.other | OAK-28297 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/255714 | - |
dc.description.abstract | We aimed to differentiate between the interictal and preictal states in epilepsy patients with focal cortical dysplasia (FCD) type-II using deep learning-based classifiers based on intracranial electroencephalography (EEG). We also investigated the practical conditions for high interictal-preictal discriminability in terms of spatiotemporal EEG characteristics and data size efficiency. Intracranial EEG recordings of nine epilepsy patients with FCD type-II (four female, five male; mean age: 10.7 years) were analyzed. Seizure onset and channel ranking were annotated by two epileptologists. We performed three consecutive interictal-preictal classification steps by varying the preictal length, number of electrodes, and sampling frequency with convolutional neural networks (CNN) using 30 s time-frequency data matrices. Classification performances were evaluated based on accuracy, F1 score, precision, and recall with respect to the above-mentioned three parameters. We found that (1) a 5 min preictal length provided the best classification performance, showing a remarkable enhancement of >13% on average compared to that with the 120 min preictal length; (2) four electrodes provided considerably high classification performance with a decrease of only approximately 1% on average compared to that with all channels; and (3) there was minimal performance change when quadrupling the sampling frequency from 128 Hz. Patient-specific performance variations were noticeable with respect to the preictal length, and three patients showed above-average performance enhancements of >28%. However, performance enhancements were low with respect to both the number of electrodes and sampling frequencies, and some patients showed at most 1–2% performance change. CNN-based classifiers from intracranial EEG recordings using a small number of electrodes and efficient sampling frequency are feasible for predicting the interictal-preictal state transition preceding seizures in epilepsy patients with FCD type-II. Preictal lengths affect the predictability in a patient-specific manner; therefore, pre-examinations for optimal preictal length will be helpful in seizure prediction. © Copyright © 2020 Chung, Jeon, Choi, Cho, Kim, Hwang and Kim. | * |
dc.language | English | * |
dc.publisher | Frontiers Media S.A. | * |
dc.subject | convolutional neural networks | * |
dc.subject | deep learning | * |
dc.subject | epilepsy surgery | * |
dc.subject | focal cortical dysplasia | * |
dc.subject | seizure prediction | * |
dc.title | Deep Convolutional Neural Network Based Interictal-Preictal Electroencephalography Prediction: Application to Focal Cortical Dysplasia Type-II | * |
dc.type | Article | * |
dc.relation.volume | 11 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | Frontiers in Neurology | * |
dc.identifier.doi | 10.3389/fneur.2020.594679 | * |
dc.identifier.wosid | WOS:000591583900001 | * |
dc.identifier.scopusid | 2-s2.0-85096392905 | * |
dc.author.google | Chung Y.G. | * |
dc.author.google | Jeon Y. | * |
dc.author.google | Choi S.A. | * |
dc.author.google | Cho A. | * |
dc.author.google | Kim H. | * |
dc.author.google | Hwang H. | * |
dc.author.google | Kim K.J. | * |
dc.contributor.scopusid | 최선아(57191539101) | * |
dc.date.modifydate | 20240315130558 | * |