Meta-Analysis of Face-Validity Indicators in Automatically Detected Dog Sleep Spindles

Visual inspection as the golden standard for sleep spindle detection has received mixed support and may not be equally viable across species and experimental conditions. Here we propose a novel intermediate strategy for approximating the quality of a detector and subject our own algorithm for spindle detection in dogs to the procedure. Automatic detections across the full range of our data (excluding only data sets with high subject overlap) were analyzed for their compliance with three face validity criteria, originally discovered by expert scorers in human EEG. These criteria are the shortness of the events (mostly below 2 s, maximum 6), the distribution of faster (> 13 Hz) spindles along the midline (higher in central and posterior derivations) and an association between amplitude and negative chirp (positive for sleep spindles and mostly unrelated for other oscillations). All three criteria were observed in the majority of data sets and sampling events (subsets of data defined by condition and/or recording channel). Replication was more consistent for data sets than for sampling events, but combined probabilities (Fisher's method) always favoured face validity compliance. Our results indicate that automatic detections in the dog, which were shown in the past to display similar to humans associations with age and cognition, also share face validity criteria with human spindles. This strengthens the dog as a model in sleep spindle research and offers additional arguments for the quality of our detection method.