Automated sleep stage and event detection algorithms using quality-controlled polysomnography annotations.

Study Objectives: To develop machine learning models for sleep stage classification, arousal detection, and respiratory event detection from overnight polysomnography, and to evaluate their performance relative to expert scorers. Methods: Overnight polysomnography recordings were obtained from healthy participants and participants referred for suspected sleep-disordered breathing. Four certified scorers completed calibration sessions and generated reference annotations for sleep stages, arousals, and respiratory events. A subset of recordings was independently annotated by all scorers to support consensus analyses, enabling direct comparison between model outputs and human inter-scorer agreement. Gradient-boosted decision tree models were trained using hand-crafted features derived from standard physiological signals. Results: Sleep stage classification achieved an accuracy of 0.840, a Cohen's kappa of 0.791, and an F1-score of 0.841, with limits of agreement for total sleep time of approximately ±0.5 h. Arousal detection achieved an F1-score of 0.733, with limits of agreement for the arousal index of approximately ±15 events/h. Respiratory event detection achieved an F1-score of 0.818, with limits of agreement for the apnea-hypopnea index also within approximately ±15 events/h. In consensus analyses, model performance was comparable to human inter-scorer agreement for sleep stages and arousals, while remaining below human inter-scorer agreement for respiratory events, despite high absolute performance relative to prior studies. Conclusions: The proposed models achieved performance approaching human-level agreement across major sleep scoring tasks. These findings indicate that high consistency in expert annotations is a key factor underlying robust model performance and support the use of quality-controlled annotations for developing reliable automated sleep analysis systems.