Classification of sleep stages from EEG, EOG and EMG signals by SSNet
Haifa Almutairi Ghulam Mubashar Hassan Amitava Datta
Classification of sleep stages plays an essential role in diagnosing
sleep-related diseases including Sleep Disorder Breathing (SDB) disease. In
this study, we propose an end-to-end deep learning architecture, named SSNet,
which comprises of two deep learning networks based on Convolutional Neuron
Networks (CNN) and Long Short Term Memory (LSTM). Both deep learning networks
extract features from the combination of Electrooculogram (EOG),
Electroencephalogram (EEG), and Electromyogram (EMG) signals, as each signal
has distinct features that help in the classification of sleep stages. The
features produced by the two-deep learning networks are concatenated to pass to
the fully connected layer for the classification. The performance of our
proposed model is evaluated by using two public datasets Sleep-EDF Expanded
dataset and ISRUC-Sleep dataset. The accuracy and Kappa coefficient are 96.36%
and 93.40% respectively, for classifying three classes of sleep stages using
Sleep-EDF Expanded dataset. Whereas, the accuracy and Kappa coefficient are
96.57% and 83.05% respectively for five classes of sleep stages using Sleep-EDF
Expanded dataset. Our model achieves the best performance in classifying sleep
stages when compared with the state-of-the-art techniques.