Using a Deep CNN for Automatic Classification of Sleep Spindles: A Preliminary Study

Abstract

In this work we applied a deep convolutional neural network to a binary classification task of clinical relevance, namely detecting sleep spindles. Specifically, we studied the conditions that are conducive of successful training on small data, emphasizing how the number of processing layers and the relative proportion of the two classes of events affect performance. We demonstrate that, in contrast with our expectations, the number of processing layers did not influence performance. Instead, the relative proportion of events affected the speed of learning but did not affect accuracy. This ceases to be the case when one class represents less than 30% of the total events, wherein training does not lead to improvement above the chance level. Overall, this preliminary study provides a picture of the dynamics that characterize training on small data, while providing further insights to explore the potential of automatic detection of sleep spindles based on deep learning.