Empirical mode decomposition based ECG enhancement and QRS detection

Area of Science:

• Biomedical Engineering
• Signal Processing

Background:

• Electrocardiogram (ECG) signals are susceptible to high and low-frequency noise, including baseline wander and power line interference.
• This noise can lead to errors in extracting crucial features like the QRS complex.
• Existing methods often require multiple processing steps.

Purpose of the Study:

• To propose a novel algorithm for ECG signal enhancement and QRS detection using Empirical Mode Decomposition (EMD).
• To address limitations of conventional techniques by offering a single-fold processing approach.

Main Methods:

• Utilized Empirical Mode Decomposition (EMD) to decompose ECG signals into Intrinsic Mode Functions (IMFs).
• Implemented a selective reconstruction technique for baseline wander correction using IMFs.
• Applied statistical peak correction and elimination of noisy lower-order IMFs for high-frequency noise removal.
• Employed nonlinear transformation on selected IMFs to enhance the QRS complex.

Main Results:

• The proposed EMD-based method effectively corrects baseline wander and removes high-frequency noise.
• QRS complex enhancement was achieved, leading to improved detection accuracy.
• The algorithm requires only a single-fold processing of each ECG signal.

Conclusions:

• The EMD-based algorithm provides an effective and efficient solution for ECG signal enhancement and QRS detection.
• This approach offers a significant improvement over conventional methods in terms of accuracy and processing time.