Classification of Sleep Apnea Syndrome From EEG Signals Using Spectrogram-Based Entropy and MLPNN Model
Yazarlar (2)
Dr. Öğr. Üyesi Kübra TANCI
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Mahmut HEKİM
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale |
| Dergi Adı | GAZİOSMANPAŞA BİLİMSEL ARAŞTIRMA DERGİSİ (GBAD) |
| Dergi ISSN | 2146-8168 |
| Dergi Tarandığı Indeksler | DIRECTORY OF RESEARCH JOURNALS INDEXING (DRJI), ROOTINDEXING ve GOOGLE SCHOLAR, COSMOS IMPACT FACTOR, INTERNATIONAL INNOVATIVE JOURNAL IMPACT FACTOR (IIJIF), INTERNATIONAL INSTITUTE OF ORGANIZED RESEARCH (I2OR), SOBIAD ATIF DİZİNİ, ACADEMIC JOURNAL INDEX |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2023 |
| Cilt No | 12 |
| Sayı | 3 |
| Sayfalar | 197 / 207 |
| Makale Linki | https://dergipark.org.tr/tr/pub/gbad |
| Özet |
| In this study, we focus on the classification of sleep apnea syndrome from EEG signals by using the spectrogram-based entropy and multilayer perceptron neural network (MLPNN) classifier model. For this aim, EEG signals with different apnea-hypopnea index (AHI) taken from Polysomnography (PSG) recordings are divided into 30 sec windows, the windowed EEG signals are decomposing into frequency sub-bands by using short time Fourier transform (STFT), and then these frequency sub-bands are normalized into the range of [0, 1]. Next, Shannon entropy values of spectrograms obtained from the normalized frequency sub-bands are used as input to the MLPNN model for the classification of sleep apnea syndrome. Finally, although high correct classification ratios were achieved in the implemented classification experiments, the highest success ratio was succeeded in the classification of severe sleep apnea syndrome. |
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