Abstract--Introduction: This study presents FetalSleepNet, the first published deep learning approach to classifying sleep states from the ovine electroencephalogram (EEG). Fetal EEG is complex to acquire and difficult and laborious to interpret consistently. However, accurate sleep stage classification may aid in the early detection of abnormal brain maturation associated with pregnancy complications (e.g. Methods: EEG electrodes were secured onto the ovine dura over the parietal cortices of 24 late-gestation fetal sheep. A lightweight deep neural network originally developed for adult EEG sleep staging was trained on the ovine EEG using transfer learning from adult EEG. A spectral equalisation-based domain adaptation strategy was used to reduce cross-domain mismatch. Results: We demonstrated that while direct transfer performed poorly, full fine-tuning combined with spectral equalisation achieved the best overall performance (accuracy: 86.6%, macro F1-score: 62.5), outperforming baseline models. Conclusions: T o the best of our knowledge, FetalSleepNet is the first deep learning framework specifically developed for automated sleep staging from the fetal EEG. Beyond the laboratory, the EEG-based sleep stage classifier functions as a label engine, enabling large-scale weak/semi-supervised labeling and distillation to facilitate training on less invasive signals that can be acquired in the clinic, such as Doppler Ultrasound or electrocardiogram data. FetalSleepNet's lightweight design makes it well suited for deployment in low-power, real-time, and wearable fetal monitoring systems. LEEP state patterns reflect fetal neurophysiological function and development [1], and are clinically relevant for detecting abnormal neurodevelopment, which may result from conditions such as chronic hypoxia, infection or hypertensive disorders of pregnancy (HDP) [2]-[4]. J. V argas-Calixto, N. Katebi, and G. D. Clifford are with the Department of Biomedical Informatics, Emory University, Atlanta, USA. Nhi Tran, R. Galinsky and S. B. Kelly are with the Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia. G. D. Clifford is also with the Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.