Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea

Background - The gold standard diagnostic test for obstructive deep apnoea (OSA) is overnight polysomnography (PSG) which is costly in terms of time a nd money. Consequently, a number of alternatives to PSG have been proposed. Oximetry is appealing because of its widespread availability and ease of a pplication. The diagnostic performance of an automated analysis algorithm b ased on falls and recovery of digitally recorded oxygen saturation was comp ared with PSG. Methods - Two hundred and forty six patients with suspected OSA were random ly selected for PSG and automated off line analysis of the digitally record ed oximeter signal. Results - The PSG derived apnoea hypopnoea index (ANI) and oximeter derived respiratory disturbance index (RDI) were highly correlated (R = 0.97). The mean (2SD) of the differences between ANI and RDI was 2.18 (12.34)/h, The sensitivity and specificity of the algorithm, depended on the AHI and RDI c riteria selected for OSA case designation. Using ease designation criteria of 15/h for AHI and RDI, the sensitivity and specificity were 98% and 88%, respectively. If the PSG derived ANI included EEG based arousals as part of the hypopnoea definition, the mean (2SD) of the differences between RDI an d ANI was -0.12 (15.62)/h and the sensitivity and specificity profile did n ot change significantly. Conclusions - In a population of patients suspected of having OSA, off line automated analysis of the oximetry signal provides a close estimate of AHI as well as excellent diagnostic sensitivity and specificity for OSA.