Electrocardiogram Quality Assessment Using Unsupervised Deep Learning.
Journal:
IEEE transactions on bio-medical engineering
Published Date:
Jan 20, 2022
Abstract
OBJECTIVE: Noise and disturbances hinder effective interpretation of recorded ECG. To identify the clean parts of a recording, free from such disturbances, various quality indicators have been developed. Previous instances of these indicators focus on human-defined desirable properties of a clean signal. The reliance on human-specified properties places an inherent limitation on the potential power of signal quality indicators. To move away from this limitation, we propose a data-driven quality indicator.