Evaluation of an automated sleep apnea scoring algorithm via the Wesper Lab home sleep apnea test

This study evaluated the performance of the Wesper Lab home sleep apnea test (HSAT) artificial intelligence (AI) automated scoring algorithm under both in-laboratory and real-world conditions. We conducted a multi-tiered validation using two datasets and three analyses. The primary analysis compared apnea-hypopnea index (AHI) and central apnea index (CAI) from Wesper Lab HSATs with simultaneous polysomnography (PSG) scored by blinded technologists (n = 44). The secondary analysis evaluated blinded scoring of raw Wesper Lab signals from the same 44 patients: first by a single scorer, then by two additional scorers to assess inter-scorer consistency. The tertiary analysis examined clinical HSATs (n = 139) in which algorithm-derived AHI was compared with expert rescoring across 11 independent clinics. Agreement metrics included Pearson correlation, Bland-Altman analysis, and confusion matrices. Primary analysis: the algorithm showed strong correlation with PSG for AHI (r = 0.90) and CAI (r = 0.82) with minimal bias on Bland-Altman analysis. Secondary analysis: the correlation was r = 0.95 with minimal bias. Across three scorers, correlation remained ≥0.93. Tertiary analysis: correlation was r = 0.98 with minimal bias. These findings demonstrate that the Wesper Lab autoscoring algorithm is a reliable tool for obstructive sleep apnea and central apnea event detection, supporting its role as an HSAT platform that enhances accessibility to sleep apnea diagnosis.