Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times

Abstract

ABSTRACTStudy ObjectivesTo investigate inter-scorer agreement and scoring time differences associated with visual and computer-assisted analysis of polysomnographic (PSG) recordings.MethodsA group of 12 expert scorers reviewed 5 PSGs that were independently selected in the context of each of the following tasks: (i) sleep stating, (ii) detection of EEG arousals, (iii) analysis of the respiratory activity, and (iv) identification of leg movements. All scorers independently reviewed the same recordings, hence resulting in 20 scoring exercises from an equal amount of different subjects. The procedure was repeated, separately, using the classical visual manual approach and a computer-assisted (semi-automatic) procedure. Resulting inter-scorer agreement and scoring times were examined and compared among the two methods.ResultsComputer-assisted sleep scoring showed a consistent and statistically relevant effect toward less time required for the completion of each of the PSG scoring tasks. Gain factors ranged from 1.26 (EEG arousals) to 2.41 (limb movements). Inter-scorer kappa agreement was also consistently increased with the use of supervised semi-automatic scoring. Specifically, agreement increased from K=0.76 to K=0.80 (sleep stages), K=0.72 to K=0.91 (limb movements), K=0.55 to K=0.66 (respiratory activity), and K=0.58 to K=0.65 (EEG arousals). Inter-scorer agreement on the examined set of diagnostic indices did also show a trend toward higher Interclass Correlation Coefficient scores when using the semi-automatic scoring approach.ConclusionsComputer-assisted analysis can improve inter-scorer agreement and scoring times associated with the review of PSG studies resulting in higher efficiency and overall quality in the diagnosis sleep disorders.