A sleep staging model on wavelet-based adaptive spectrogram reconstruction and light weight CNN

Effective methods for automatic sleep staging are important for diagnosis and treatment of sleep disorders. EEG has weak signal properties and complex frequency components during the transition of sleep stages. Wavelet-based adaptive spectrogram reconstruction (WASR) by seed growth is utilized to capture dominant time-frequency patterns of sleep EEG. We introduced variant energy from Teager operator in WASR to capture hidden dynamic patterns of EEG, which produced additional spectrograms. These spectrograms enabled a light weight CNN to detect and extract finer details of different sleep stages, which improved the feature representation of EEG. With specially designed depthwise separable convolution, the light weight CNN achieved more robust sleep stage classification. Experimental results on Sleep-EDF 20 dataset showed that our proposed model yielded overall accuracy of 87.6%, F1-score of 82.1%, and Cohen kappa of 0.83, which is competitive compared with baselines with reduced computation cost.