Adaptive matched filtering for QRS detection

Key Points

Adaptive filtering improves QRS detection in ECG signals affected by motion artifact, indicating significant progress.
Results show substantial enhancement over traditional matched filter methods in high-noise environments.
Observational analysis using ECG data highlights challenges posed by muscle noise and motion artifacts.
Improvements in QRS detection may enable better clinical monitoring in dynamic conditions.

Abstract

The authors have investigated the use of an adaptive filter to whiten the noise in the ECG (electrocardiogram) signal and adjust the matched filter response accordingly. They applied a simple QRS detection strategy to the filtered signal and evaluated the QRS detector with ECG data containing severe motion artifact and muscle noise. Preliminary results indicate that in the presence of severe motion artifact, adaptive matched filtering for QRS detection represents a significant improvement over application of a matched filter with a white noise assumption.>

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Adaptive matched filtering for QRS detection

Key Points

Abstract

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