Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG
This enables convenient, on-device sleep monitoring without servers, addressing the resource-intensive nature of traditional polysomnography for users needing accessible sleep analysis.
The authors tackled real-time sleep staging by developing a deep learning system that runs entirely on a smartphone using wearable EEG data, achieving 83.5% accuracy in five-class classification on the Sleep-EDF dataset.
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.