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Artifact Correction in Short-Term HRV during Strenuous Physical Exercise
Aleksandra Królak1, Tomasz Wiktorski2, Magnus Friestad Bjørkavoll-Bergseth3
1Institute of Electronics, Lodz University of Technology, 93-005 Lodz, Poland.
Sensors (Basel, Switzerland)
|November 11, 2020
Summary
This study introduces an automated algorithm for correcting artifacts in heart rate variability (HRV) data from personal monitoring devices. The new method effectively handles more artifacts than existing solutions with minimal impact on HRV measures.
Area of Science:
- Cardiovascular physiology
- Biomedical signal processing
- Wearable technology
Background:
- Heart rate variability (HRV) analysis is crucial for detecting health issues.
- Current HRV analysis often requires controlled conditions and manual artifact correction.
- The rise of personal monitoring devices (PMDs) enables continuous HRV analysis in real-world settings.
Purpose of the Study:
- To develop an automated artifact correction algorithm for real-life HRV data.
- To minimize the impact of artifact correction on HRV measures.
- To improve artifact handling capabilities compared to existing methods.
Main Methods:
- Development of a novel automated artifact correction algorithm.
- Validation using two datasets: a recreational bicycle race and a laboratory setting.
- Utilized data from GPS watches (PMDs) paired with chest straps and power sensors.
Main Results:
- The proposed algorithm corrects more artifacts than existing solutions without manual intervention or parameter tuning.
- Introduced error in HRV measures for peak correction and short gaps is comparable to state-of-the-art methods.
- The algorithm demonstrates better curve shape matching to ground truth for longer gaps compared to cubic interpolation.
Conclusions:
- The developed algorithm offers a promising solution for automated artifact correction in real-life HRV data.
- It shows potential for improving the reliability of HRV analysis from personal monitoring devices.
- Further development may enhance performance for longer data gaps.
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