Deep CNN for EEG in Sleep Staging

Abstract

There is recently a large amount of deep learning algorithms developed for sleep staging using the signals, especially EEG, recorded by sleep monitoring devices for sleep related patients. Among them, those based on CNN architecture have some advantages over the others. This paper mainly discusses the CNN-based sleep staging algorithms for EEG, and meanwhile considers their upstream monitoring devices and the downstream clinical tasks. The relationship between these three parts of the sleep staging in the clinical context is discussed in this study. According to the overall picture built for the three parts, suggestions are proposed for the scientists and physicians working in these fields. Considering the clinical demands, the CNN-based sleep stage classifiers are expected to perform better on real-time analysis and generalization, while keep improving the precision on two different modality of signals both including EEG. The monitoring devices, correspondingly, should deal with the trade-off between lightweightness and the amount of information recorded. These suggestions should offer an overall view for the scientists to have a clear knowledge about how to work together to contribute to the effective treatment for patients.