Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Computational Cardiology

Background:

• Modern electrocardiography (ECG) requires accurate R-peak detection, especially with advances in wearable and low-cost devices.
• Noisy ECG signals pose a significant challenge for reliable R-peak identification.

Purpose of the Study:

• To introduce a new R-peak detection method for noisy ECG signals.
• To improve the accuracy and robustness of R-peak detection in electrocardiography.

Main Methods:

• Utilized visibility graph transformation to map time-series ECG data to a graph structure.
• Applied signal weighting based on node connectivity to amplify R-peaks and suppress noise.
• Employed a simple thresholding procedure (e.g., Pan and Tompkins) for final R-peak identification.

Main Results:

• The proposed method effectively amplifies R-peaks while suppressing noise and other signal components.
• Benchmarking demonstrated significant performance improvement over common R-peak detectors on a noisy database.
• The method exhibits linear time complexity with respect to the number of segments analyzed.

Conclusions:

• The visibility graph-based R-peak detection method offers a promising solution for accurate ECG analysis.
• This approach is particularly beneficial for applications using wearable and low-cost devices.
• The method provides a significant advancement in detecting R-peaks in challenging, noisy ECG data.