A robust generalizable device-agnostic deep learning model for sleep-wake determination from triaxial wrist accelerometry

• URL: http://arxiv.org/abs/2512.01986v1
• Date: Mon, 01 Dec 2025 18:43:51 GMT
• Title: A robust generalizable device-agnostic deep learning model for sleep-wake determination from triaxial wrist accelerometry
• Authors: Nasim Montazeri, Stone Yang, Dominik Luszczynski, John Zhang, Dharmendra Gurve, Andrew Centen, Maged Goubran, Andrew Lim,
• Abstract summary: We developed a robust deep learning model for to detect sleep-wakefulness from triaxial accelerometry.<n>We evaluated its validity across three devices and in a large adult population spanning a wide range of ages with and without sleep disorders.
• Score: 0.816568176049513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Study Objectives: Wrist accelerometry is widely used for inferring sleep-wake state. Previous works demonstrated poor wake detection, without cross-device generalizability and validation in different age range and sleep disorders. We developed a robust deep learning model for to detect sleep-wakefulness from triaxial accelerometry and evaluated its validity across three devices and in a large adult population spanning a wide range of ages with and without sleep disorders. Methods: We collected wrist accelerometry simultaneous to polysomnography (PSG) in 453 adults undergoing clinical sleep testing at a tertiary care sleep laboratory, using three devices. We extracted features in 30-second epochs and trained a 3-class model to detect wake, sleep, and sleep with arousals, which was then collapsed into wake vs. sleep using a decision tree. To enhance wake detection, the model was specifically trained on randomly selected subjects with low sleep efficiency and/or high arousal index from one device recording and then tested on the remaining recordings. Results: The model showed high performance with F1 Score of 0.86, sensitivity (sleep) of 0.87, and specificity (wakefulness) of 0.78, and significant and moderate correlation to PSG in predicting total sleep time (R=0.69) and sleep efficiency (R=0.63). Model performance was robust to the presence of sleep disorders, including sleep apnea and periodic limb movements in sleep, and was consistent across all three models of accelerometer. Conclusions: We present a deep model to detect sleep-wakefulness from actigraphy in adults with relative robustness to the presence of sleep disorders and generalizability across diverse commonly used wrist accelerometers.

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