Spektrogram-Tabanlı Entropi ve MLPNN Modeli Kullanılarak EEG İşaretlerinden Uyku Apne Sendromu Sınıflandırması

Year 2023, Volume: 12 Issue: 3, 197 - 207, 31.12.2023

Kübra Tancı Mahmut Hekim

Abstract

Bu çalışmada, spektrogram-tabanlı entropi ve çok katmanlı algılayıcı sinir ağı (MLPNN) sınıflandırıcı modelini kullanarak EEG işaretlerinden uyku apne sendromunun sınıflandırılmasına odaklanılmaktadır. Bu amaç için, Polisomnografi (PSG) kayıtlarından alınan farklı apne-hipoapne indeksine (AHI) sahip EEG işaretleri 30 saniyelik pencerelere bölünmekte, pencereli EEG işaretleri kısa zamanlı Fourier dönüşümü (STFT) kullanılarak frekans alt bantlarına ayrıştırılmakta ve bu altbantlar [0, 1] aralığına normalize edilmektedir. Daha sonra, normalize edilen frekans altbantlarından elde edilen spektrogramların Shannon entropi değerleri uyku apne sendromu sınıflandırılması için MLPNN modeline giriş olarak kullanılmaktadır. Sonuç olarak, gerçekleştirilen sınıflandırma deneylerinde yüksek doğru sınıflandırma oranları elde edilmiş olsa da en yüksek başarı oranına şiddetli uyku apne sendromunun sınıflandırılmasında ulaşılmıştır.

Keywords

EEG işareti, Uyku apnesi, Spektrogram, Entropi, MLPNN.

Classification of Sleep Apnea Syndrome From EEG Signals Using Spectrogram-Based Entropy and MLPNN Model

Abstract

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.

Keywords

EEG signals, sleep apnea, Spectrogram, entropy, MLPNN.

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