Bhavin R. Sheth and Adam Jones from the University of Houston have developed a new home-based sleep study method using a single-lead ECG, potentially replacing the traditional, more cumbersome polysomnography.

The method improves understanding and monitoring of sleep patterns.

Bhavin R. Sheth, an associate professor of electrical and computer engineering at the University of Houston, along with former student Adam Jones, has developed a revolutionary method for sleep stage classification. This innovative approach has the potential to replace the current gold standard, polysomnography, which is often cumbersome due to its extensive wiring and the need for clinical settings. Their new procedure, utilizing a single-lead electrocardiography-based deep learning neural network, can be conveniently performed by users at home.

If you’ve ever had a problem sleeping, and ended up in a sleep lab, you know the polysomnography test is anything but restful. With a multitude of leads – sensors and wires – dangling from every part of your body, you are asked to sleep, which is a state difficult to reach without such encumbrance, nearly impossible with it.

But what if the number of those electrodes – attached from your scalp to your heart – was reduced to simply two?

Benefits of the New Method

“We have successfully demonstrated that our method achieves expert-level agreement with the gold-standard polysomnography without the need for expensive and cumbersome equipment and a clinician to score the test,” reports Sheth in Computers in Biology and Medicine. “This advancement challenges the traditional reliance on electroencephalography (or EEG) for reliable sleep staging and paves the way for more accessible, cost-effective sleep studies.”

Even more, by enabling access to high-quality sleep analysis outside clinical settings, Adam and Bhavin’s research holds the potential to expand the reach of sleep medicine significantly.

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. Although commercial devices like the Apple Watch, Fitbit, and Oura Ring track sleep, their performance is well below that of polysomnography.

The electrocardiography-based model was trained on 4000 recordings from subjects 5–90 years old. They showed that the model is robust and performs just as well as a clinician-scoring polysomnography.

“Our method significantly outperforms current research and commercial devices that do not use EEG and achieve gold-standard levels of agreement using only a single lead of electrocardiography data,” said Sheth, who is also a member of the UH Center for NeuroEngineering and Cognitive Systems.

“It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.”

To that end, Jones made the complete source code freely available for researchers, clinicians, and anyone else interested at https://cardiosomnography.com

Reference: “Expert-level sleep staging using an electrocardiography-only feed-forward neural network” by Adam M. Jones, Laurent Itti and Bhavin R. Sheth, 29 April 2024, Computers in Biology and Medicine.
DOI: 10.1016/j.compbiomed.2024.108545