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Sleep staging by hyperdimensional dense networks

Traditionally, sleep staging is done by medical experts, but computer aid will improve sleeping evaluation. We propose a mathematically-motivated algorithm based on Dense Convolutional Networks that encodes polysomnography (PSG) recordings into a very high-dimensional vector space to perform sleep-stage scoring. We emphasize the flexibility of our model as it provides a framework to analyze single or multi-channel signals without relying on any statistical information about the dataset. To prove the feasibility of our model we show results attaining comparable or better accuracy than current state-of-the-art models at a fraction of the time and very limited training data.