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Effects of Missing Data on Heart Rate Variability Metrics
Diego Cajal1,2, David Hernando1,2, Jesús Lázaro1,2
1Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain.
Insights
Data loss in wearable heart rate variability (HRV) analysis impacts metrics differently. Correction without gap filling suits burst data loss for time-domain HRV, while gap filling benefits scattered data loss and frequency-domain HRV.
Area of Science:
- Cardiovascular physiology
- Biomedical engineering
- Signal processing
Background:
- Heart rate variability (HRV) analysis is crucial in clinical settings.
- Wearable devices present challenges for HRV due to poor signal quality and motion artifacts, causing data loss.
- Understanding data loss impact on HRV metrics is vital for reliable health monitoring.
Purpose of the Study:
- To investigate the effects of data loss on time-domain, frequency-domain, and Poincaré plot HRV metrics.
- To propose and evaluate a novel gap-filling method for mitigating data loss in HRV analysis.
- To determine optimal data correction strategies based on data loss patterns and HRV metrics.
Main Methods:
- Simulated and real-world datasets with scattered and burst missing beats were analyzed.
- Proposed gap-filling method was compared against existing approaches.
- Photoplethysmography (PPG) data from Apple Watch during rest and stress protocols were utilized.
Main Results:
- Correction without gap filling is optimal for SDNN, RMSSD, and Poincaré plots with predominant burst missing beats.
- Gap-filling methods are superior for scattered missing beats and all frequency-domain HRV metrics.
- Significant differences (p<0.05) were observed based on data loss type and correction method.
Conclusions:
- The choice of data correction method for HRV analysis depends on the nature of data loss (scattered vs. burst) and the specific HRV metrics of interest.
- Findings guide the development of robust HRV applications for wearable health monitoring.
- Optimal strategies balance missing data tolerance with desired metric accuracy.
Abstract:
Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.
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