QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

Florian KristofMaximilian KapseckerLeon NissenJames BrimicombeMartin R CowieZixuan DingAndrew DymondStephan M JonasHannah Clair LindénGregory Y H LipKate WilliamsJonathan MantPeter H Charltonnull null

Published in: PLOS digital health (2024)

The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

Keyphrases

decision making
machine learning
cardiac resynchronization therapy
deep learning
sensitive detection