Key result
A proposed QRS detection algorithm utilizing low pass, difference, and moving average filters achieved sensitivities ranging from 85% to 91% in noisy ECG signals.
Why the study?
ECG signals are typically affected by artifacts requiring manual assessment or reference signals, prompting the need for automatic recognition of QRS complexes.
Population
Three cardiogram patterns, each consisting of 20 samples of 20 seconds duration, distorted by various types…
Design
Other
Authors
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Proposed QRS algorithm may support automated ECG analysis; leaves open prospective validation before clinical adoption.
A proposed less computational algorithm using a series of filters and an adaptive threshold function achieved 85-91% sensitivity in detecting QRS complexes in noisy ECG signals.
Younes et al. (2021) studied ECG signal processing (n=3). Proposed QRS detection algorithm was evaluated on Sensitivity of QRS detection. A proposed QRS detection algorithm utilizing low pass, difference, and moving average filters achieved sensitivities ranging from 85% to 91% in noisy ECG signals.
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