Artifact processing during exercise testing.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Artifacts in exercise electrocardiograms (ECGs) pose a significant challenge, particularly during high-intensity exercise.
• Distinguishing true ECG signals from noise and interference is crucial for accurate cardiac assessment.
• Existing methods struggle with artifact reduction, impacting diagnostic reliability.

Purpose of the Study:

• To develop and evaluate novel algorithms for robust ECG signal processing during exercise.
• To address the persistent problem of artifact contamination in exercise ECGs.
• To improve the accuracy of cardiac function analysis in challenging exercise conditions.

Main Methods:

• Introduction of the Finite Impulse Response Residual Filtering (FRF) algorithm for noise reduction.
• Development of the Intelligent Lead Switch algorithm to leverage multilead ECG redundancy.
• FRF algorithm: Subtracts median beat, filters residual, and re-adds median beat.
• Intelligent Lead Switch: Selects optimal leads for improved QRS detection and analysis.

Main Results:

• The FRF algorithm effectively reduces baseline wander and muscle noise with minimal QRS complex distortion.
• The Intelligent Lead Switch algorithm enhances QRS detection accuracy by utilizing lead redundancy.
• Improved performance in heart rate calculation, ST segment evaluation, and arrhythmia classification is demonstrated.

Conclusions:

• The FRF and Intelligent Lead Switch algorithms offer significant improvements in exercise ECG signal processing.
• These methods enhance the reliability of diagnostic information extracted from exercise ECGs.
• The developed algorithms are valuable tools for clinical exercise testing and cardiac research.