0315 Inter- And Intra-expert Variability In Sleep Scoring: Comparison Between Visual And Automatic Analysis. (27th April 2018) - Muto, V; Berthomier, C; Schmidt, C; Vandewalle, G; Jaspar, M; Devillers, J; Chellappa, S; Meyer, C; Phillips, C; Berthomier, P; Prado, J; Benoit, O; et al.
Abstract: Introduction: Visual sleep scoring (VS) is affected by inter-expert (difference in scoring between several scorers working on the same recording) and intra-expert variability (evolution in the way to score of a given expert when compared with a reference). Our aim was to quantify inter and intra-expert sleep scoring variability in a group of 6 experts -working at the same sleep center and trained to homogenize their sleep scoring- by using the validated automatic scoring (AS) algorithm ASEEGA, which is fully reproducible by design, as a reference. Methods: Data were collected in 24 healthy young male participants (mean age 21.6 ± 2.5 years). 4 recordings (data set 1, DS1) were scored by the 6 experts (24 visual scorings) according to the AASM criteria, and by AS, which is based on the analysis of the single EEG channel Cz-Pz. Other 88 recordings (DS2) were scored a few weeks later by the same experts (88 visual scorings) and AS. The epoch-by-epoch agreements (concordance and Cohen kappa coefficient) were computed between all VS, and between VS and AS. Results: Inter-expert agreement on DS1 decreased as the number of experts increased, from 86% for mean pairwise agreement down to 69% for all 6 experts. Adding AS to the pool of experts barely changed the kappa value, from 0.81 to 0.79. A systematic decrease of the agreements was observed between AS and each single expert between DS1 and DS2 (-3.7% on average). Conclusion: Inter-expert differences are not restricted to a small proportion of specific epochs that are difficult to score, even when the expert team is very homogeneous. Intra-expert variability is highlighted by the systematic agreement decrease across datasets, and can be interpreted as a scoring drift over time. Even if autoscoring neither provides any ground truth, nor can improve the inter-scorer agreement, it can efficiently cope with the intra-scorer variability, when the AS used is perfectly reproducible and largely insensitive to experimental conditions. These properties are mandatory when dealing with large dataset, making autoscoring methods a sensible option. Support (If Any): None.