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A Novel R-Peak Detection Algorithm
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DOI：10.1109/ACCESS.2025.3643153.png)
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Abstract
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Accurate detection of R-peaks in electrocardiogram (ECG) signals is essential for cardiac analysis and diagnosis. While existing algorithms often rely on complex transformations or machine learning, we revisit the classical philosophy of using simple and interpretable signal features. We propose a method based on two physiologically meaningful signal features: detrended relative height and local slope pattern. These features are integrated into a robust scoring system with adaptive thresholding. Evaluated on the MIT-BIH Arrhythmia Database, our algorithm uses only a single channel and achieves high sensitivity (99.69%) and specificity (99.82%), performing well within the class of interpretable and transparent methods. The results demonstrate that lightweight, transparent approaches can provide reliable performance and serve as effective baselines for further development.
Keywords:
Feature extraction
Electrocardiography
Time series analysis
Detection algorithms
Convolution
Filter banks
Accuracy
Signal processing algorithms
Physiology
Noise
Electrocardiogram signal
R-peak detection
signal processing
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect.png)
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