دورية أكاديمية
Algorithm for automatic EEG classification according to the epilepsy type: Benign focal childhood epilepsy and structural focal epilepsy
| العنوان: | Algorithm for automatic EEG classification according to the epilepsy type: Benign focal childhood epilepsy and structural focal epilepsy |
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| المؤلفون: | Misiukas Misiūnas, Andrius Vytautas, Meškauskas, Tadas, Samaitienė, Rūta |
| المصدر: | Biomedical signal processing and control, Oxford : Elsevier Ireland Ltd, 2019, vol. 48, p. 118-127 ; ISSN 1746-8094 ; eISSN 1746-8108 |
| سنة النشر: | 2019 |
| المجموعة: | LSRC VL (Lithuanian Social Research Centre Virtual Library) / LSTC VB (Lietuvos socialinių tyrimų centras virtualią biblioteką) |
| مصطلحات موضوعية: | EEG, Epilepsy, Epileptiform discharge, Spike, Machine learning, Artificial neural network |
| الوصف: | Rationale: It is still not clear if there are EEG parameters that may be related to the epilepsy etiology in epilepsies presenting with rolandic spikes. Rolandic spikes are not pathognomonic for rolandic epilepsy and could be related to the area of discharges itself. The initial hypothesis was that even visually identical spikes have some difference, because of the different etiology. Objective: The aim of the study was to find the differences in rolandic spike morphology in two epilepsy groups, different by etiology, but presenting with visually identical spikes. Methods: A novel algorithm for automatic classification of interictal electroencephalogram (EEG) rolandic spikes according to the epilepsy type (Group I – patients with benign focal childhood epilepsy, self-limiting, with no causal lesion in the brain, Group II – patients with structural focal epilepsy) is proposed. The algorithm consists of three stages: 1) EEG spike detection, 2) determination of EEG spike parameters, 3) classification of EEG by epilepsy type based on estimated spike parameters. Automatic classification method is defined by artificial neural network. The algorithm has been trained and tested on a large data sample provided by Children’s Hospital, Affiliate of Vilnius University Hospital Santaros Klinikos. Only those EEGs that were visually identical and inaccessible for manual clustering to the groups according the visual spike morphology and contained 50 or more spikes have been analyzed. Training and testing pools have been selected as non overlapping (containing different patients) data sets. Results: The proposed methodology let us to achieve up to 75% of accuracy of classification of EEG. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | http://vu.lvb.lt/VU:ELABAPDB32018396&prefLang=en_USTest |
| الإتاحة: | https://doi.org/10.1016/j.bspc.2018.10.006Test http://vu.lvb.lt/VU:ELABAPDB32018396&prefLang=en_USTest |
| رقم الانضمام: | edsbas.A12ACD12 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |