A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models

•Manual scoring of sleep-wake states from EEG and other recordings in animals is time consuming.•We developed two machine learning algorithms for automated sleep-wake scoring in different species.•A Convolutional Neural Network algorithm performed best for scoring sleep-wake states in non-human primates and dogs.•A Random Forest algorithm performed best for scoring sleep-wake states in mice and rats. Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. Typically, it involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual identification, or scoring, of the sleep-wake states from these recordings is time-consuming. We sought to develop software for automated sleep-wake scoring capable of processing large databases of multi-channel signal recordings in a range of species. We used a large historical database of signal recordings and scores in non-human primates, dogs, mice, and rats, to develop a deep Convolutional Neural Network (CNN) classification algorithm for automatically scoring sleep-wake states. We compared the performance of the CNN algorithm with that of a widely used Machine Learning algorithm, Random Forest (RF). CNN accuracy in sleep-wake scoring of data in non-human primates and dogs was significantly higher than RF accuracy (0.75 vs. 0.66 for non-human primates and 0.73 vs. 0.64 for dogs). In rodents, the difference between CNN and RF was smaller: 0.83 vs. 0.81 for mice and 0.78 vs. 0.77 for rats. The variability of CNN accuracy was lower than that of RF for non-human primates, dogs and mice but similar for rats. Deep Learning algorithms have not been previously evaluated across a range of species for animal sleep-wake scoring. We recommend use of CNN for sleep-wake scoring in non-human primates and dogs, and RF for sleep-wake scoring in rodents.