Graph-informed convolutional autoencoder to classify brain responses during sleep.

Automated machine-learning algorithms that analyze biomedical signals have been used to identify sleep patterns and health issues. However, their performance is often suboptimal, especially when dealing with imbalanced datasets. In this paper, we present a robust sleep state (SlS) classification algorithm utilizing electroencephalogram (EEG) signals. To this aim, we pre-processed EEG recordings from 33 healthy subjects. Then, functional connectivity features and recurrence quantification analysis were extracted from sub-bands. The graphical representation was calculated from phase locking value, coherence, and phase-amplitude coupling. Statistical analysis was used to select features with -values of less than 0.05. These features were compared between four states: wakefulness, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep during presenting auditory stimuli, and REM sleep without stimuli. Eighteen types of different stimuli including instrumental and natural sounds were presented to participants during REM. The selected significant features were used to train a novel deep-learning classifiers. We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the functional connectivity features. Furthermore, an attention layer based on recurrence rate features extracted from EEGs was incorporated into the GICA classifier to enhance the dynamic ability of the model. The proposed model was assessed by comparing it to baseline systems in the literature. The accuracy of the SlS-GICA classifier is 99.92% on the significant feature set. This achievement could be considered in real-time and automatic applications to develop new therapeutic strategies for sleep-related disorders.