Applicability of multiresolution wavelet analysis for QRS-waves detection.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Accurate detection of the QRS complex in electrocardiographic (ECG) signals is crucial for diagnosing cardiac arrhythmias.
• Existing QRS detection algorithms often struggle with signal noise and artifacts, impacting diagnostic reliability.

Purpose of the Study:

• To develop a highly effective QRS-detector for ECG signals.
• To enhance QRS detection accuracy using multiresolution wavelet analysis, nonlinear transforms, and adaptive thresholding.

Main Methods:

• Implementation of a novel QRS detection algorithm.
• Utilizing multiresolution wavelet analysis for signal decomposition.
• Application of nonlinear transforms and adaptive thresholding for robust feature extraction.
• Validation using simulated noisy ECG signals and the Physionet MIT-BIH Arrhythmia database.

Main Results:

• The proposed QRS-detector demonstrated high efficiency in processing simulated ECG signals with intensive noise and artifacts.
• Comparative analysis showed the proposed method outperformed other well-known QRS detection algorithms.
• Successful verification on clinical ECG recordings from a widely recognized arrhythmia database.

Conclusions:

• The developed QRS-detector offers a robust and effective solution for heartbeat detection in challenging ECG signals.
• The combination of wavelet analysis and adaptive thresholding provides a powerful approach for improving ECG signal analysis.
• This method holds potential for enhancing the accuracy of automated cardiac arrhythmia diagnosis.