R-R Interval Outlier Exclusion Method Based on Statistical ECG Values Targeting HRV Analysis Using Wearable ECG...

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Wearable electrocardiogram (ECG) devices are prone to artifacts causing misdetected R waves.
• These misdetected R waves lead to inaccurate R-R Interval (RRI) measurements.
• Existing methods struggle to effectively identify and exclude artifact-induced RRI outliers.

Purpose of the Study:

• To develop and validate an R-R Interval (RRI) outlier exclusion method.
• To accurately identify and remove misdetected R waves caused by artifacts in ECG data.
• To enhance the reliability of heart rate variability (HRV) analysis from wearable ECG devices.

Main Methods:

• Proposed an RRI outlier exclusion method utilizing statistical ECG values.
• The method statistically distinguishes misdetected R waves associated with artifacts.
• Annotates detected R waves with artifact occurrence status for RRI measurement evaluation.

Main Results:

• Experimental results confirmed the effectiveness of the proposed method.
• The method significantly improved the accuracy of time-domain HRV measures.
• Frequency-domain HRV measures also showed improved accuracy compared to conventional methods.

Conclusions:

• The developed RRI outlier exclusion method effectively handles artifact-induced misdetections.
• This approach enhances the precision of HRV analysis from wearable ECG data.
• The method offers a robust solution for reliable cardiovascular monitoring.