Validating a smart bed against polysomnography for sleep apnea detection.

Sleep-disordered breathing (SDB), including obstructive sleep apnea (OSA) and central sleep apnea (CSA), significantly impairs sleep quality and overall well-being. This study evaluates a novel algorithm, developed and trained by the authors, using ballistocardiography (BCG) data collected from a non-intrusive smart bed platform. The algorithm aims to detect SDB events and estimate whether the apnea-hypopnea index (AHI) is ≥ 15, indicative of moderate to severe apnea. We analyzed data from 104 participants (48 males, 56 females; 21 with AHI ≥ 15 (13 males, 8 females), 83 with AHI < 15) by comparing algorithm-generated AHI estimates with standard polysomnography (PSG)-based AHI measurements. The algorithm achieved an accuracy of 83.3% in identifying individuals with moderate-to-severe apnea (AHI ≥ 15), demonstrating a sensitivity of 76% and specificity of 85%. Visual inspection of signals during apnea episodes, particularly those related to CSA, confirmed the algorithm's capability to capture meaningful physiological patterns. The unobtrusive design of the smart bed facilitates longitudinal sleep monitoring without requiring cumbersome equipment or specialized technical expertise. Future research will focus on validating the algorithm using multi-night, real-world data to enhance its generalizability. Smart beds show promise for early detection and personalized management of SDB, potentially improving clinical outcomes through improved tracking and targeted intervention.