Effects of ECG Signal Processing on the Inverse Problem of Electrocardiography.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging

Background:

• The inverse problem of electrocardiography (ECG) is crucial for reconstructing cardiac electrical activity but is inherently ill-posed.
• Model errors, such as signal noise, can compromise the accuracy of these reconstructions.
• The sensitivity of the ECG inverse problem to various signal processing techniques remains largely unexplored.

Purpose of the Study:

• To evaluate the impact of different signal processing techniques on the accuracy of solving the inverse problem of electrocardiography.
• To determine how specific processing steps, including noise removal and baseline correction, influence the reconstruction of cardiac electrical activity.

Main Methods:

• Experimental data from a Langendorff-perfused pig heart (n=1) in a human torso-shaped tank with 128 surface electrodes was utilized.
• Multiple signal processing methods were applied, categorized into high-frequency noise removal, baseline drift removal, and signal averaging, resulting in 72 unique signal sets.
• The inverse problem was solved for each processed signal set, and reconstructed signals were quantitatively compared to directly recorded epicardial electrograms.

Main Results:

• ECG signal processing methods demonstrably influenced reconstruction accuracy.
• Removal of baseline drift significantly altered the magnitude of reconstructed electrograms.
• The presence of high-frequency noise was found to impact the accuracy of the activation time derived from the reconstructed signals (p<0.05).

Conclusions:

• Signal processing choices have a substantial and differential impact on the accuracy of the inverse problem of electrocardiography.
• Baseline drift removal critically affects the amplitude of reconstructed cardiac signals.
• High-frequency noise is a key factor influencing the temporal accuracy (activation time) of reconstructed cardiac electrical activity.