Real-time Smartphone-based Sleep Staging using 1-Channel EEG
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bsn_19 (1).pdf
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Author(s)
Koushik, Abhay
Amores Fernandez, Judith
Maes, Patricia
Date Issued
May 2019
Journal
Proceedings of the 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Publisher
IEEE
Citation
Koushik, Abhay, Judith Amores, and Pattie Maes. "Real-time Smartphone-based Sleep Staging using 1-Channel EEG." IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), May 2019, Chicago, Illinois, USA, IEEE © 2019 by IEEE
Version
Author's final manuscript
Abstract
Automatic and real-time sleep scoring is necessary to develop user interfaces that trigger stimuli in specific sleep stages. However, most automatic sleep scoring systems have been focused on offline data analysis. 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 Convolutional Neural Network (CNN). 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, smartphone-based 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 5-stage classification of sleep stages using the open Sleep-EDF dataset. For comparison, inter-rater reliability among sleep-scoring experts is about 80% (Cohen's k=0\pmb.68 to \pmb0.76). We further propose an on-device metric independent of the deep learning model which increases the average accuracy of classifying deep-sleep (N3) to more than 97.2% on 4 test nights using power spectral analysis. Keyword: Sleep; Electroencephalography; Real-time systems; Brain modeling; Electrooculography; Spectral analysis; Training
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/bsn.2019.8771091
