Contactless Sleep Apnea Detection with Bodyseismography

Sleep apnea is a prevalent yet underdiagnosed sleep disorder. Existing monitoring approaches largely rely on wearable devices or smartphone-based systems, which can be uncomfortable, require active user engagement, or be sensitive to environmental conditions. In this paper, we present a feasibility study of a contactless, engagement-free sleep-apnea screening approach using an under-bed horizontal (head-to-foot) seismic sensor that captures micro vibrations from respiration, heartbeat, and movement. We show that the horizontal axis provides a clearer respiratory signature than the vertical axis, enabling respiration-focused signal analysis. From the seismic signal, we extract 15 features across three families—respiratory heartbeat, and movement—and formulate a minute-level three-class classification task: Normal, OSA+hypopnea , and CSA. Using a strict patient-independent 5-fold cross-validation protocol on 116 subjects, we achieve 85.5% balanced accuracy and 80.6% macro F1 with a random forest classifier; class-wise balanced accuracies are 96.3% (Normal), 75.0% (OSA+hypopnea), and 85.2% (CSA). Overall, this work demonstrates the feasibility of under-bed seismic sensing for window-level apnea-related state classification.