A Rule-based Automatic Algorithm for Detecting Total Sleep Time using Pulse Oximetry Signal

This study proposed a lightweight and interpretable rule-based framework to estimate TST from overnight finger-tip pulse oximetry signals only. After pre-processing, different features were extracted from each 30 second epoch and the rule-based framework classified each epoch as either sleep or wake. After applying TST correction, the framework finally estimated the TST as the output. Performance evaluation demonstrated significant correlation between estimated and annotated TST values (Pearson’s correlation coefficient, r = 0.804 and Lin’s concordance correlation coefficient, CCC = 0.760). Bland-Altman plot demonstrated small mean bias (0.24 hours) with a narrow limits of agreement (-0.95 to 1.44 hours). The proposed framework provides reliable and computationally efficient TST estimation, thus a promising approach for portable and home-based sleep monitoring applications.