An R-peak detection method that uses an SVD filter and a search back system

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Accurate R-peak detection is crucial for analyzing electrocardiogram (ECG) signals.
• Existing methods can struggle with noise and baseline wander, impacting diagnostic reliability.

Purpose of the Study:

• To develop and evaluate a robust R-peak detection method for ECG signals.
• To improve detection accuracy, especially in noisy datasets, by combining signal processing techniques.

Main Methods:

• A two-phase detection process involving pre-processing and decision stages.
• Pre-processing utilized a singular value decomposition (SVD) filter, Butterworth High Pass Filter (HPF), moving average (MA), and squaring.
• The decision phase incorporated a modified search back system based on R-R intervals (RRIs).

Main Results:

• The modified search back system reduced the R-peak detection error rate to 0.29% from 1.34%.
• Achieved high performance metrics: 99.47% sensitivity, 99.47% positive predictivity, and 1.05% detection error.
• Demonstrated effective R-peak detection even in signals with substantial noise, improving overall signal quality.

Conclusions:

• The proposed SVD filter and modified search back system offer a highly accurate and effective method for R-peak detection in ECG signals.
• This technique enhances signal quality and reduces errors, making it suitable for clinical applications, particularly with noisy data.