Ayush Tripathi, Arnav Gupta, Wolfgang Ganglberger, Samuel T. Waters, Haoqi Sun, Samaneh Nasiri, Ayan Mitra, Katie L Stone, Emmanuel Mignot, Dennis W. Hwang, Matthew A. Reyna, Lynn Marie Trotti, Gari D. Clifford, Kiran Maski, Umakanth Katwa, Robert J Thomas, M Brandon Westover
STUDY OBJECTIVES: Manual sleep staging in pediatric populations is challenging due to developmental variability and limited scoring consistency, especially in infants and toddlers. We developed a multimodal deep learning model for pediatric sleep staging and evaluated its performance across a broad age range and diverse clinical subgroups. METHODS: We trained a U-Net-inspired encoder-decoder model (pediatric SleepNet) using 9-channel input signals: Electroencephalography (EEG), Electrooculography(EOG), and chin Electromyography (EMG) using 35-epoch segments from clinical pediatric polysomnograms (PSGs). Models were trained separately on three age groups (<6 months, 6-12 months, >1 year) using 9,150 PSGs, with 2,455 PSGs reserved for validation. Evaluation was conducted on 3,804 held-out test recordings. Performance was compared with U-Sleep and the Complete Artificial Intelligence Sleep Report (CAISR), and stratified analyses were performed across ages, sexes, and seven ICD-10-based disease categories. External validation was conducted on two independent datasets, CHAT and PATS. RESULTS: pediatric SleepNet achieved robust performance across all age groups, with mean Cohen's Kappa increasing from 0.49 (0-6 months) to 0.72 (>12 years). It significantly outperformed U-Sleep and CAISR across early developmental stages. 3-class staging yielded mean Cohen's Kappa increasing from 0.66 (0-6 months) to 0.79 (>12 years). Sex-based differences were negligible. However, significant reductions in performance were observed in children with epilepsy, Down syndrome, hydrocephalus, and other neurodevelopmental conditions. External validation yielded Kappa values >0.69 comparable to the internal test set. CONCLUSIONS: pediatric SleepNet demonstrates reliable sleep staging across pediatric development. Its robust performance across age, disease, and external datasets supports its potential for clinical and research use in pediatric sleep medicine.