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

Authors : Kübra Tanci, Mahmut Hekim

Pages : 197-207

View : 89 | Download : 56

Publication Date : 2023-12-31

Article Type : Research Paper

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 işareti, Uyku apnesi, Spektrogram, Entropi, MLPNN

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