Why the study?
AF and other abnormal heart rhythms are linked to fatal cardiovascular diseases, making automated, robust methods to reliably detect AF, sinus, and non-sinus rhythms valuable.
A novel multi-layer classifier architecture using adaptive boosting accurately detects atrial fibrillation and other abnormal rhythms from short, single-lead ECGs, achieving joint first place in the PhysioNet 2017 Challenge.
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Merits prospective clinical validation for AF screening; leaves open generalizability across diverse populations and settings.
Mukherjee et al. (2019) studied this question.
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