Design and performance evaluation of a dynamic wearable ECG system based on the MC-DST method

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Cardiovascular Monitoring

Background:

• Continuous electrocardiogram (ECG) monitoring is crucial for diagnosing cardiac conditions.
• Existing wearable systems often struggle with motion artifacts and electrode contact impedance.
• Improving R-peak detection accuracy and signal quality in dynamic environments is essential.

Purpose of the Study:

• To develop a wearable ECG monitoring system with high-fidelity signal acquisition.
• To implement an advanced algorithm for artifact suppression and R-peak detection.
• To validate the system's performance in real-world dynamic monitoring scenarios.

Main Methods:

• Utilized silver fiber fabric electrodes with contact impedance < 10 Ω.
• Employed STM32F411RCT6 and ADS1292 chips for data acquisition and processing.
• Developed a hybrid Morphological Contour-Dual Slope Threshold (MC-DST) algorithm for signal analysis.
• Integrated Bluetooth 5.0 and a 64 GB SD card for continuous data logging.

Main Results:

• Achieved 24-hour continuous operation with low power consumption.
• The MC-DST algorithm effectively eliminated baseline drift and suppressed motion artifacts.
• Demonstrated high R-peak detection accuracy of 99.38%.
• Achieved a sensitivity of 99.52% in dynamic monitoring tests.

Conclusions:

• The developed wearable ECG system provides reliable, long-term cardiac monitoring.
• The novel MC-DST algorithm significantly enhances signal quality and detection accuracy.
• The system shows great potential for clinical applications and remote patient monitoring.