A recent US study showed that analyzing the heart's electrical activity during sleep using artificial intelligence techniques may help predict the risk of cardiovascular diseases, such as atrial fibrillation, heart failure, and stroke, years before they appear.
Research Methodology and Data
The scientific website ScienceAlert reported on Wednesday that the study conducted by researchers from Emory University relied on training an AI model using data from approximately 16,000 individuals who underwent sleep tests, then testing it on two independent groups comprising an additional 23,000 individuals.
Accuracy of Risk Prediction
The results showed that analyzing electrocardiogram recordings, collected during sleep using a single electrode on the chest, helped improve the prediction of future health risks, even after accounting for known risk factors such as age, diabetes, and high blood pressure.
Results and Future Applications
According to the study published in the journal SLEEP, the risk scores determined by the model were associated with an increased likelihood of developing atrial fibrillation, stroke, and heart failure over the following ten years, with the strength of the association varying depending on the disease and patient group.
These results suggest the potential to utilize sleep test data in the future to identify individuals most at risk of cardiovascular diseases, which may aid in early detection. However, using this technique in medical practice requires further studies to verify its accuracy and effectiveness.
Source: Syrian Arab News Agency (SANA)
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