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The detection of human sleep stages is widely used in the diagnosis andintervention of neurological and psychiatric diseases. Some patients with deepbrain stimulator implanted could have their neural activities recorded from thedeep brain. Sleep stage classification based on deep brain recording has greatpotential to provide more precise treatment for patients. The accuracy andgeneralizability of existing sleep stage classifiers based on local fieldpotentials are still limited. We proposed an applicable cross-modal transferlearning method for sleep stage classification with implanted devices. Thisend-to-end deep learning model contained cross-modal self-supervised featurerepresentation, self-attention, and classification framework. We tested themodel with deep brain recording data from 12 patients with Parkinson's disease.The best total accuracy reached 83.2% for sleep stage classification. Resultsshowed speech self-supervised features catch the conversion pattern of sleepstages effectively. We provide a new method on transfer learning from acousticsignals to local field potentials. This method supports an effective solutionfor the insufficient scale of clinical data. This sleep stage classificationmodel could be adapted to chronic and continuous monitor sleep for Parkinson'spatients in daily life, and potentially utilized for more precise treatment indeep brain-machine interfaces, such as closed-loop deep brain stimulation.

Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals