To make it easier to diagnose and study sleep problems, engineers have created a device to monitor sleep stages without sensors attached to the body. Their device instead uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM).
The researchers previously developed radio-based sensors that enable them to remotely measure vital signs and behaviors that can be indicators of health. These sensors consist of a wireless device, about the size of a laptop computer, that emits low-power radio frequency (RF) signals. As the radio waves reflect off of the body, any slight movement of the body alters the frequency of the reflected waves. Analyzing those waves can reveal vital signs such as pulse and breathing rate.
After developing those sensors, the researchers thought that a similar approach could also be useful for monitoring sleep, which is currently done while patients spend the night in a sleep lab hooked up to monitors such as electroencephalography (EEG) machines.
Recent advances in artificial intelligence have made it possible to train computer algorithms known as deep neural networks to extract and analyze information from complex datasets, such as the radio signals obtained from the researchers’ sensor. However, these signals have a great deal of information that is irrelevant to sleep and can be confusing to existing algorithms. The researchers had to come up with a new AI algorithm based on deep neural networks, which eliminates the irrelevant information.