TY - JOUR
T1 - A deep transfer learning approach for sleep stage classification and sleep apnea detection using wrist-worn consumer sleep technologies
AU - Olsen, Mads
AU - Zeitzer, Jamie M
AU - Nakase-Richardson, Risa
AU - Musgrave, Valerie H
AU - Sorensen, Helge B D
AU - Mignot, Emmanuel
AU - Jennum, Poul J
PY - 2024
Y1 - 2024
N2 - Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.
AB - Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.
KW - Accelerometry/instrumentation
KW - Adult
KW - Aged
KW - Deep Learning
KW - Female
KW - Humans
KW - Male
KW - Middle Aged
KW - Polysomnography/instrumentation
KW - Signal Processing, Computer-Assisted
KW - Sleep Apnea Syndromes/diagnosis
KW - Sleep Stages/physiology
KW - Wearable Electronic Devices
KW - Wrist
UR - http://www.scopus.com/inward/record.url?scp=85188440980&partnerID=8YFLogxK
U2 - 10.1109/TBME.2024.3378480
DO - 10.1109/TBME.2024.3378480
M3 - Journal article
C2 - 38498753
SN - 0018-9294
VL - 71
SP - 2506
EP - 2517
JO - IEEE transactions on bio-medical engineering
JF - IEEE transactions on bio-medical engineering
IS - 8
ER -