An Image Based Prediction Model for Sleep Stage Identification

Human brain undergoes state changes during sleep which produces distinctive signal patterns when recorded by electroencephalography (EEG). Automatic identification of these stages is crucial to diagnosing and treating sleep related disorders. We propose an image processing based technique for automatic identification of sleep stages from EEG signals. We generate two dimensional image representations from the high dynamic range Fourier transform features of the one dimensional EEG signals. Using these representations, we learn a deep and dense convolutional neural network (CNN) model for prediction. The key advantage of the proposed method is its seamless use of the existing well studied and powerful deep CNN models designed for computer vision problems. Experiments on the popular Sleep-EDF database show that the proposed method significantly outperforms the compared methods for automatic sleep stage identification.

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