An epileptic seizure detection system based on cepstral analysis and generalized regression neural network
Yazarlar (4)
Doç. Dr. Erdem Yavuz Bursa Teknik Üniversitesi, Türkiye
Doç. Dr. Mustafa Cem KASAPBAŞI İstanbul Ticaret Üniversitesi, Türkiye
Can Eyüpoğlu Milli Savunma Üniversitesi, Türkiye
Rıfat Yazıcı İstanbul Ticaret Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biocybernetics and Biomedical Engineering (Q4) | ||
| Dergi ISSN | 0208-5216 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2018 |
| Cilt / Sayı / Sayfa | 38 / 2 / 201–216 | DOI | 10.1016/j.bbe.2018.01.002 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0208521617303716 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
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| Özet |
| This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroencephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the … |
| Anahtar Kelimeler |
| Cepstral analysis | Electroencephalogram | Epileptic seizure detection | Generalized regression neural network |
Science Direct
An epileptic seizure detection system based on cepstral analysis and generalized regression neural network
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 53 |
| Scopus | 59 |
| Google Scholar | 57 |
| Google Scholar | 50 |