Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the...