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A preprocessing tool for removing artifact from cardiac RR interval recordings using three-dimensional spatial
Nicolas J C Stapelberg1,2, David L Neumann1, David H K Shum1
1School of Applied Psychology and Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
Psychophysiology
|January 12, 2016
Summary
A new algorithm effectively detects and removes artifact in cardiac RR interval data for heart rate variability (HRV) analysis. This method preserves physiological data, ensuring accurate HRV measures in clinical recordings.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Artifacts are prevalent in cardiac RR interval data, complicating heart rate variability (HRV) analysis.
- Accurate HRV assessment relies on clean, artifact-free RR interval data.
Purpose of the Study:
- To introduce and validate a novel algorithm for detecting and interpolating artifacts in RR interval data.
- To assess the impact of artifact removal on time and frequency domain HRV metrics.
Main Methods:
- Developed a 3D spatial distribution mapping algorithm for artifact detection and interpolation.
- Validated the algorithm using artificial RR interval data with controlled artifact levels (0.5%-10%).
- Assessed the algorithm's performance on 69 human 24-h cardiac recordings and its impact on 10 HRV metrics.
Main Results:
- Achieved high sensitivity (0.84) and specificity (1.00) in artifact removal for both artificial and human cardiac data.
- Artifact removal resulted in minimal changes to HRV metrics (0%-2.5% variation).
- The algorithm successfully removed artifacts without significantly altering physiological data.
Conclusions:
- The novel preprocessing tool effectively removes artifacts from 24-h cardiac recordings.
- The tool minimally impacts physiological data, preserving the integrity of HRV measures.
- This algorithm offers a reliable solution for improving the quality of HRV analysis.
