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In Journal of neuroscience methods ; h5-index 0.0

BACKGROUND : 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.

NEW METHOD : 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).

RESULTS : 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.

COMPARISON WITH EXISTING METHODS : Deep Learning algorithms have not been previously evaluated across a range of species for animal sleep-wake scoring.

CONCLUSIONS : We recommend use of CNN for sleep-wake scoring in non-human primates and dogs, and RF for sleep-wake scoring in rodents.

Vladimir Svetnik, Ting-Chuan Wa, Yuting Xu, Bryan J Hansen, Steven V Fox


Convolutional Neural Network, Machine learning, Random Forest, sleep-wake scoring