Key result
The proposed QRS detection algorithm achieved a sensitivity ranging from 85% to 91% across three different cardiogram signals with varying levels of noise and distortion.
Why the study?
ECG signals are normally affected by artifacts that require manual assessment or reference signals, motivating less computational automatic recognition of QRS complexes.
Design
Algorithm development and validation study
Authors
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May support automated QRS detection in noisy signals; leaves open prospective clinical validation before practice use.
A proposed low-computational algorithm using a series of filters and an adaptive threshold function successfully detects QRS complexes in ECG signals with 85-91% sensitivity, even in the presence of noise.
Younes et al. (2020) studied ECG QRS detection (n=60). Proposed QRS detection algorithm was evaluated on Sensitivity of QRS detection. The proposed QRS detection algorithm achieved a sensitivity ranging from 85% to 91% across three different cardiogram signals with varying levels of noise and distortion.
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