Why the study?
Can an automated algorithm using signal quality indices and machine learning accurately detect poor-quality ECGs?
Population
1500 12-lead ECGs from the PhysioNet Challenge 2011 dataset (1000 training, 500 test)
Comparison
Automated algorithm using signal quality metrics… vs Human annotator labels
Authors
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May enable automated ECG quality screening in practice; leaves open need for prospective validation across settings.
Can an automated algorithm using signal quality indices and machine learning accurately detect poor-quality ECGs?
An automated algorithm using signal quality metrics and machine learning can accurately identify clinically unacceptable ECGs with 97% accuracy on test data.
Clifford et al. (2012) studied this question.
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