Revolutionizing sleep monitoring using Deep Learning: a new wearable technology accurately measuring sleep stages from pulsatile signals.

 Sleep staging is an essential component in the diagnosis of sleep disorders and the management of sleep health. Researchers from the Laboratory for Artificial Intelligence in Medicine, headed by Dr. Joachim A. Behar showed that it is possible to accurately measure sleep stages using photoplethysmography (PPG) signals recorded with standard oximetry. 

This opens the door for the development of wearable devices that can be used to diagnose sleep disorders and improve sleep health. 

The study, which analyzed 23,055 hours of continuous nocturnal data, demonstrates the feasibility of using the PPG signal to robustly perform sleep staging, which is traditionally a labor-intensive process.

The research was published in IEEE Journal of Biomedical and Health Informatics (JBHI). The authors of the paper are Kevin Kotzen, Dr. Peter H. Charlton, Sharon Salabi, Lea Amar, Prof. Amir Landesberg, and Dr. Joachim A. Behar. 

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