Real-time, automatic, open-source sleep stage classification system using single EEG for mice.

ABSTRACT: We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitgeber time. To accommodate historical information from each subject, we included a long short-term memory recurrent neural network in combination with the universal features. The resulting system (UTSN-L) achieved 90% overall accuracy and 81% multi-class Matthews Correlation Coefficient, with particularly high-quality judgements for rapid eye movement sleep (91% sensitivity and 98% specificity). This system can enable automatic real-time interventions during rapid eye movement sleep, which has been difficult due to its relatively low abundance a

SUBMITTER: Tezuka T 

PROVIDER: S-EPMC8160151 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature