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Impact of observational error on heart rate variability analysis.
Monika Petelczyc1, Jan Jakub Gierałtowski1, Barbara Żogała-Siudem2
1Faculty of Physics, Warsaw University of Technology, Koszykowa 75, PL-00-662, Warsaw, Poland.
Heliyon
|May 29, 2020
Summary
Observational errors in heart rate variability (HRV) analysis can significantly impact results. Our study identified specific HRV parameters, like pNN50 and frequency markers, as most sensitive to these errors, crucial for accurate population studies.
Area of Science:
- Cardiology
- Biomedical Engineering
- Physiology
Background:
- Heart rate variability (HRV) analysis is vital for assessing autonomic nervous system function.
- Observational errors, stemming from factors like sampling frequency and QRS detection, can compromise HRV data integrity.
- Understanding these errors is crucial for reliable interpretation of HRV in clinical and research settings.
Purpose of the Study:
- To model and quantify observational errors in HRV measurements.
- To identify HRV parameters most and least susceptible to specific sources of observational error.
- To provide guidance on considering observational error in population-based HRV studies.
Main Methods:
- Development of a mathematical model for observational error in HRV.
- Application of Monte Carlo simulations to assess error impact on various HRV parameters.
- Analysis of time, frequency, and nonlinear HRV domains.
Main Results:
- The pNN50 parameter and frequency-domain markers were found to be highly sensitive to observational errors.
- Other time-domain parameters and Detrended Fluctuation Analysis (DFA) slopes demonstrated greater resistance to these errors.
- The study quantified the magnitude of observational error across different HRV metrics.
Conclusions:
- Observational error significantly affects specific HRV parameters, necessitating careful consideration in research.
- Researchers using diverse equipment or studying patients with varied arrhythmias should account for potential HRV data scatter.
- The findings advocate for standardized protocols and error assessment in large-scale HRV investigations.
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