# Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

arXiv: https://arxiv.org/abs/1811.10111 · HTML: https://mlsanity.com/paper/1811.10111/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals](https://mlsanity.com/paper/2605.18837/) (2026)

## Abstract (arXiv)

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.