A transformer-based AI model analyzes eight-hour sleep signals from brain, movement, cardiac, and respiratory data to generate summaries, which are then used to classify sleep stages for the entire night.
Credit: Benjamin Fox, PhD candidate at the Icahn School of Medicine at Mount Sinai.
Credit: Benjamin Fox, PhD candidate at the Icahn School of Medicine at Mount Sinai.
Novel technique streamlines sleep analysis and supports future clinical tools to detect sleep disorders and other health risks
Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night’s sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. Details on their findings were reported in the March 13 online issue of the journal Sleep.