Towards real-time sleep stage classification: A deep learning approach leveraging PPG and ECG

20260 citationsJournal Articlehybrid Open Access

Authors

Thomas Dyhre Nielsen · Aalborg University

Anders Bruun · University College of Northern Denmark

Abstract

This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.

Topics & Keywords

Obstructive Sleep Apnea Research EEG and Brain-Computer Interfaces Non-Invasive Vital Sign Monitoring

Publication Details

Published in: Physiological Measurement

DOI: 10.1088/1361-6579/ae5458

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Towards real-time sleep stage classification: A deep learning approach leveraging PPG and ECG

Authors

Abstract

Topics & Keywords

Publication Details