Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Expert Systems with Applications (Q1) | ||
| Dergi ISSN | 0957-4174 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-06-2020 |
| Cilt / Sayı / Sayfa | 148 / 1 / 1–9 | DOI | 10.1016/j.eswa.2020.113239 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0957417420300658 | ||
| Özet |
| Electroencephalography signals obtained from the brain‘s electrical activity are commonly used for the diagnosis of neurological diseases. These signals indicate the electrical activity in the brain and contain information about the brain. Epilepsy, one of the most important diseases in the brain, manifests itself as a result of abnormal pathological oscillating activity of a group of neurons in the brain. Automated systems that employed the electroencephalography signals are being developed for the assessment and diagnosis of epileptic seizures. The aim of this study is to focus on the effectiveness of stacking ensemble approach based model for predicting whether there is epileptic seizure or not. So, this study enables the readers and researchers to examine the proposed stacking ensemble model. The benchmark clinical dataset provided by Bonn University was used to assess the proposed model. Comparative … |
| Anahtar Kelimeler |
| Deep neural networks | Electroencephalography signals | Epileptic seizure | K-fold cross-validation | Performance improvement | Stacking approach |
Science Direct
Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 153 |
| Google Scholar | 181 |