ECG-based automated detection of sleep apnea using deep neural networks and hidden markov models
Researchers have developed a deep learning framework that uses ECG signals to detect obstructive sleep apnea with high accuracy. The method integrates multiple neural network architectures and a Hidden Markov Model to improve diagnostic reliability.
Why it matters
This technology offers a more accessible and cost-effective alternative to traditional, resource-heavy sleep apnea diagnostic methods.
Scientific Reports ( 2026 ) Cite this article
We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.
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