Analysis and visualization of sleep stages based on deep neural networks.

ABSTRACT: Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep he

SUBMITTER: Krauss P 

PROVIDER: S-EPMC7973384 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature