Data-driven study of mouse sleep-stages using Restricted Boltzmann Machines
Rodents play an important role in sleep studies since they are the most easily available low-cost animal species sharing most genes and gene functions with humans. The scoring of sleep stages in these studies is usually based on a manual analysis of long-lasting recordings of the brain electrical activity and the activity of skeletal muscles. Hence, there is a great need for tools automating the investigation over large experiments. In this paper we present a way of analyzing this huge amount of electrophysiological data using unsupervised learning models. We show how employing latent variable models, like Restricted Boltzmann Machines, we can define different sleep-wakefulness sub-stages reflected by regularities in the data, without any prior knowledge. Our analysis shows that we can effectively discover meaningful feature representations which characterize sleep stages at a finer level than those commonly used by the experts. These feature representations can be also used to further characterize different mouse genotypes, without incurring biases of experts like in a classical analysis setup where predefined rules limit the discovery of novel insights.