A new algorithm engineered by researchers at the U.K.'s Queen Mary University of London (QMUL) and University College London could eventually enable everyday wearables to alert users to potentially fatal changes in heart rhythm.
The algorithm can recognize electrocardiogram (ECG) readings correlating with the risk of hospitalization or death resulting from an abnormal heart rhythm.
The team obtained a reference for normal T waves (the time for heart's ventricles to relax once they have pumped blood out) on an ECG from data on some 24,000 U.K. Biobank Imaging study participants, then applied the algorithm to ECG data from over 50,000 other participants.
Results indicated that people with the biggest T-wave changes over time were more likely to be hospitalized or die from ventricular arrhythmias.
QMUL's Julia Ramirez said the algorithm is "better at predicting risk of arrhythmia than standard ECG risk markers."
From Queen Mary University of London (U.K.)
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