Reliability of Family Dogs’ Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification
Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, 1117 Budapest, Hungary
Department of Ethology, Eötvös Loránd University, 1117 Budapest, Hungary
Department of General Psychology, Pázmány Péter Catholic University, 1088 Budapest, Hungary
Institute for Computer Science and Control, Informatics Laboratory, 1111 Budapest, Hungary
Department of Cognitive Psychology, Eötvös Loránd University, 1053 Budapest, Hungary
Author to whom correspondence should be addressed.
Received: 20 February 2020
Revised: 14 May 2020
Accepted: 18 May 2020
Published: 26 May 2020
Sleep alterations are known to be severe accompanying symptoms of many human psychiatric conditions, and validated clinical protocols are in place for their diagnosis and treatment. However, sleep monitoring is not yet part of standard veterinary practice, and the possible importance of sleep-related physiological alterations for certain behavioral problems in pets is seriously understudied. Recently, a non-invasive electroencephalography (EEG) method was developed for pet dogs that is well-suited for untrained individuals. This so called polysomnography protocol could easily be implemented in veterinary diagnosis. However, in order to make the procedure more effective and standardized, methodological questions about the validity and reliability of data processing need to be answered. As a first step, the present study tests the effect of several factors on the manual scoring of the different sleep stages (a standard procedure adopted from human studies). Scoring the same recordings but varying the number of EEG channels visible to the scorer (emulating the difference between single channel versus four channel recordings) resulted in significant differences in hypnograms. This finding suggests that using more recording electrodes may provide a more complete picture of dog brain electrophysiological activity. Visual sleep staging by three different expert raters also did not provide a full agreement, but despite this, there were no significant differences between raters in important output values such as sleep structure and the spectral features of the EEG. This suggests that the non-invasive canine polysomnography method is ready to be implemented for veterinary use, but there is room for further refinement of the data processing. Here, we describe which parts of the sleep recording yield the lowest agreement and present the first form of an automated method that can reliably distinguish awake from sleep stages and could thus accelerate the time-consuming manual data processing. The translation of the findings into clinical practice will open the door to the more effective diagnosis and treatment of disorders with sleep-related implications.