R-point detection for noise affected ECG recording through signal segmentation

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) signals are crucial for diagnosing cardiac conditions.
• Noise interference significantly degrades ECG signal quality, hindering accurate analysis.
• Existing R-peak detection methods may struggle with noise-affected ECG data.

Purpose of the Study:

• To develop and evaluate a novel algorithm for filtering noise in ECG signals.
• To assess the performance of the proposed method for R-peak recognition.
• To compare the proposed algorithm against manual cardiologist marking and an established automatic method.

Main Methods:

• A novel noise filtering approach based on signal approximation using linear segments.
• Application of the method for accurate R-peak detection in ECG signals.
• Comparative analysis against manual R-peak identification by cardiologists and the Laguna's method.

Main Results:

• The proposed algorithm effectively filters noise from ECG signals.
• R-peak recognition using the novel method shows promising results.
• The algorithm achieved a smaller mean error and superior error distribution compared to the Laguna's method.

Conclusions:

• The proposed linear segment approximation method offers an effective solution for noise reduction in ECG signals.
• This approach enhances the accuracy of R-peak recognition, outperforming the Laguna's method.
• The findings suggest potential for improved automated ECG analysis in clinical settings.