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Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG
- Publication Year :
- 2018
-
Abstract
- 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.<br />Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1811.10111
- Document Type :
- Working Paper