First, when annotations on a target dataset are unavailable, which different data characteristics affect the sleep stage scoring performance the most and by how much?
Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others.
This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.
Ranked #4 on Sleep Stage Detection on MASS SS3
Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.