Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time Sleep Staging using Deep Learning on a Smartphone for a
  Wearable EEG   We present the first real-time sleep staging system that uses deep learning
without the need for servers in a smartphone application for a wearable EEG. We
employ real-time adaptation of a single channel Electroencephalography (EEG) to
infer from a Time-Distributed 1-D Deep Convolutional Neural Network.
Polysomnography (PSG)-the gold standard for sleep staging, requires a human
scorer and is both complex and resource-intensive. Our work demonstrates an
end-to-end on-smartphone pipeline that can infer sleep stages in just single
30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation
for five-class classification of sleep stages using the open Sleep-EDF dataset.

33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg