Towards Translational Sleep Staging: A Cross-Species Deep-Learning Model for Rodent and Human EEG

Study Objectives Automated sleep staging underpins clinical sleep assessment and translational neuroscience, yet most data analyses work addresses human and animal data separately. We tested whether a seizure-oriented machine learning framework can be repurposed for three-state sleep staging in humans and rats, and whether models trained solely on rodent data can be applied directly to human recordings using an explicit cross-species montage. Methods We used the PySeizure, a standardised EEG preprocessing and seizure-detection framework, together with TinySleepNet as the core classifier. Models were trained and evaluated on the Sleep-EDF expanded Sleep Cassette subset (three classes: wake, non-rapid eye movement sleep, rapid eye movement sleep), then applied without fine-tuning to the Sleep Telemetry subset. The same pipeline was used on a SYNGAP1 rat dataset with analogous three-state labels. A novel human–rat electroencephalography montage mapped rat electrodes to putative human scalp homologues, enabling direct application of rat-trained models to Sleep Cassette. Results Within Sleep Cassette, the accuracy in three-stage sleep classification was 0.95. Applying this model directly to Sleep Telemetry yielded an accuracy of 0.89. On the rodent dataset, accuracy was 0.78. When the rat-trained model was applied directly to Sleep Cassette, accuracy was 0.68. Conclusions A single deep learning pipeline can support robust three-state sleep staging in humans and rodents and retains meaningful performance under both human cross-subset and rat-to-human transfer without any retraining or fine-tuning. The rat-trained model’s above-chance performance on human data, achieved without human training samples, shows that rodent-derived representations can contribute directly to human sleep staging when constrained by an anatomically informed montage, linking preclinical rodent recordings and clinical human sleep studies.