University of Houston, associate professor of electrical and computer engineering Bhavin R. Sheth and former student Adam Jones, have introduced a groundbreaking approach to sleep stage classification that could replace the current gold standard in sleep testing, the cumbersome polysomnography, which uses a myriad of wires and is performed in a clinic. Their new procedure, which can be performed at home by the user, uses a single-lead electrocardiography-based deep learning neural network.
"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 for reliable sleep staging and paves the way for more accessible, cost-effective sleep studies.
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.
Source: Healthcare Press (healthcarepress.net)
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