Motion artifact reduction in electrocardiogram using adaptive filtering based on half cell potential monitoring

Area of Science:

• Biomedical Engineering
• Cardiology
• Signal Processing

Background:

• Electrocardiograms (ECGs) are crucial for diagnosing chronic diseases and guiding cardio-fitness therapy.
• Noise and motion artifacts in ECG signals can impair automatic analysis algorithms.
• Accurate ECG interpretation requires robust methods to handle signal interferences.

Purpose of the Study:

• To evaluate a novel method for measuring heart contact point (HCP) variations caused by motion artifacts.
• To assess the effectiveness of a wearable sensing device for direct HCP variation monitoring.
• To improve the reliability of ECG analysis in the presence of motion artifacts.

Main Methods:

• A two-channel wearable sensing device was developed.
• One channel measured ECG using a differential amplifier.
• A second channel monitored motion artifacts using a modified electrode and the same differential amplifier.
• Adaptive filtering was employed for noise reduction, utilizing a reference signal.
• Direct measurement of HCP variations was performed.

Main Results:

• The proposed device successfully monitored motion artifacts.
• Adaptive filtering effectively reduced noise based on the reference signal.
• Direct measurement of HCP variations was achieved without additional sensors.
• The method demonstrated potential for enhancing ECG signal quality.

Conclusions:

• Direct monitoring of HCP variations in motion artifacts is feasible with the proposed wearable device.
• This approach can mitigate the impact of noise on automatic ECG analysis.
• The findings suggest a promising method for improving diagnostic accuracy and therapy guidance in cardio-fitness.