Inter-expert and intra-expert reliability in sleep spindle scoring

•Spindle identification is a difficult task, and more than one sleep expert is needed to reliably score spindles in EEG data.•The reliability of sleep staging may be improved by improving the reliability of spindle scoring, particularly for the discrimination of stage N1 and N2 sleep.•Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system.

ObjectivesTo measure the inter-expert and intra-expert agreement in sleep spindle scoring, and to quantify how many experts are needed to build a reliable dataset of sleep spindle scorings.MethodsThe EEG dataset was comprised of 400 randomly selected 115 s segments of stage 2 sleep from 110 sleeping subjects in the general population (57 ± 8, range: 42–72 years). To assess expert agreement, a total of 24 Registered Polysomnographic Technologists (RPSGTs) scored spindles in a subset of the EEG dataset at a single electrode location (C3-M2). Intra-expert and inter-expert agreements were calculated as F1-scores, Cohen’s kappa (κ), and intra-class correlation coefficient (ICC).ResultsWe found an average intra-expert F1-score agreement of 72 ± 7% (κ: 0.66 ± 0.07). The average inter-expert agreement was 61 ± 6% (κ: 0.52 ± 0.07). Amplitude and frequency of discrete spindles were calculated with higher reliability than the estimation of spindle duration. Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system.ConclusionsWe estimate that 2–3 experts are needed to build a spindle scoring dataset with ‘substantial’ reliability (κ: 0.61–0.8), and 4 or more experts are needed to build a dataset with ‘almost perfect’ reliability (κ: 0.81–1).SignificanceSpindle scoring is a critical part of sleep staging, and spindles are believed to play an important role in development, aging, and diseases of the nervous system.