Adaptive coherent averaging for real-time electrocardiogram enhancement.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) signals are crucial for diagnosing cardiac conditions.
• Broad-band interference and quasi-periodic signals can degrade ECG quality.
• Existing methods may struggle to remove interference while preserving signal morphology.

Purpose of the Study:

• To develop an adaptive coherent averaging structure for robust ECG signal denoising.
• To improve the signal-to-noise ratio (SNR) of ECG signals.
• To ensure preservation of essential ECG morphological features.

Main Methods:

• Implementation of an adaptive coherent averaging structure.
• Utilizing Least Mean-Square (LMS) and Recursive Least-Square (RLS) adaptive algorithms.
• Analysis and comparison of LMS and RLS algorithm performance.

Main Results:

• The proposed structure effectively removes broad-band interference from ECG signals.
• Significant improvement in signal-to-noise ratio (SNR) was achieved.
• Morphological features of the ECG signal were preserved.
• An optimized hybrid implementation combining LMS and RLS was developed.

Conclusions:

• The adaptive coherent averaging structure offers robust ECG signal denoising.
• The hybrid implementation provides an optimized solution for real-time applications.
• The scalable, cascaded nature of the structure is well-suited for hardware implementation.