TY - GEN
T1 - Automatic sleep staging using a small-footprint sensor array and recurrent-convolutional neural networks
AU - Coon, William G.
AU - Punjabi, Naresh M.
N1 - Funding Information:
*This work was supported by the John Hopkins University (JHU) Applied Physics Lab (APL) and School of Medicine (SOM) under a contract with Glaxo Smith Klein (GSK). The content is solely the responsibility of the authors and does not represent the official views of GSK.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/4
Y1 - 2021/5/4
N2 - The accelerating trend towards personalized 'pre-cision medicine' and tele-healthcare is revolutionizing the practice of medicine and giving the individual unprecedented access to their own health data. At the same time, a widening gap between wakeful health (ex. physical activity) and nocturnal health (sleep) has revealed the need for accurate, reliable and automated methods to measure sleep in the home. Here we describe a small-footprint sensor array, using electrode stickers that can be self-applied to the forehead, in conjunction with an automated scoring algorithm that achieves accuracies on par with trained human experts (77% agreement using a five-class taxonomy). Compared to alternatives, this approach avoids the low signal-to-noise ratios of dry-contact scalp electrodes while also circumventing the need to measure through hair. Critically, it does not require a trained human expert, either to apply the electrodes or to translate the signals into a useful description of sleep patterns. Taken together, this represents an exciting step forward towards affordable, reliable, and accurate in-the-home sleep assessment.
AB - The accelerating trend towards personalized 'pre-cision medicine' and tele-healthcare is revolutionizing the practice of medicine and giving the individual unprecedented access to their own health data. At the same time, a widening gap between wakeful health (ex. physical activity) and nocturnal health (sleep) has revealed the need for accurate, reliable and automated methods to measure sleep in the home. Here we describe a small-footprint sensor array, using electrode stickers that can be self-applied to the forehead, in conjunction with an automated scoring algorithm that achieves accuracies on par with trained human experts (77% agreement using a five-class taxonomy). Compared to alternatives, this approach avoids the low signal-to-noise ratios of dry-contact scalp electrodes while also circumventing the need to measure through hair. Critically, it does not require a trained human expert, either to apply the electrodes or to translate the signals into a useful description of sleep patterns. Taken together, this represents an exciting step forward towards affordable, reliable, and accurate in-the-home sleep assessment.
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U2 - 10.1109/NER49283.2021.9441432
DO - 10.1109/NER49283.2021.9441432
M3 - Conference contribution
AN - SCOPUS:85107483574
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 1144
EP - 1147
BT - 2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PB - IEEE Computer Society
T2 - 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Y2 - 4 May 2021 through 6 May 2021
ER -