A review of electrocardiogram filtering.

Area of Science:

• Biomedical Engineering
• Medical Signal Processing

Background:

• Analog and digital signal processing are crucial for high-quality electrocardiogram (ECG) signals.
• Inaccurate ECG preprocessing can impede signal analysis, interpretation, and diagnosis.

Purpose of the Study:

• To investigate inaccuracies in ECG preprocessing filters.
• To identify issues impacting efficient ECG interpretation and diagnosis.
• To suggest improvements for ECG data preprocessing standards.

Main Methods:

• Analysis of 4 key ECG preprocessing applications: anti-aliasing, baseline wander suppression, line frequency rejection, and muscle artifact reduction.
• Examination of filter-related issues including linear phase, aliasing, distortion, ringing, and signal attenuation.

Main Results:

• ECG data often presents overlapping signal and noise power spectra, requiring filters to balance noise removal with signal integrity.
• Filter inaccuracies can distort desired ECG signals, potentially affecting diagnostic outcomes.

Conclusions:

• Existing ECG preprocessing standards and guidelines require updates to address filter inaccuracies.
• Optimizing filters is essential to prevent adverse impacts on ECG diagnosis and interpretation.
• Improved preprocessing enhances the reliability of ECG analysis for clinical decision-making.