Key result
Application of different Kubios filters, especially strong and very strong filters, impacted the quantification of HRV-derived parameters in COPD patients.
Why the study?
Noise, trends, ectopic beats, and artifacts directly affect HRV quality, warranting signal correction to assess the impact of different filters in Kubios software on short-term HRV analysis in COPD.
Does the choice of corrective filter in Kubios software affect the quantification of short-term HRV parameters in COPD patients?
Cross-Sectional (n=81)
Does the choice of corrective filter in Kubios software affect the quantification of short-term HRV parameters in COPD patients?
The choice of corrective filter in Kubios software significantly impacts the quantification of HRV parameters, highlighting the need for standardized methodological approaches in HRV analysis.
Answers are grounded in this paper and cite their sources.
Filter choice alters short-term HRV metrics in COPD; leaves open optimal preprocessing standardization for clinical use.
The existence of noise, trends, ectopic beats, and artifacts can directly affect HRV quality, warranting signal correction. Our objective was to analyze the impact of different filters in the Kubios software on short-term HRV analysis in a COPD. A cross-sectional study involving 81 COPD patients was allocated into four groups according to clinical status and severity: (1) non-exacerbated COPD GOLD I-II, (2) non-exacerbated COPD GOLD III-IV, (3) exacerbated COPD GOLD I-II, and (4) exacerbated COPD GOLD I-II. The R-R interval signal was registered in the supine position over 10 min using a Polar S810i telemetry system; Kubios software was used for short-term HRV analysis using the six filters available. There were significant differences ( p < 0.05) in HRV-derived parameters in the time domain across the Kubios filters, especially in the strong and very strong filters. In the frequency domain and non-linear analysis, only the very strong filter was significant when compared to the other filters in some variables ( p < 0.05). The application of different Kubios filters had a significant impact on the quantification of HRV-derived parameters obtained from short-term recordings in linear and non-linear analyses.
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Santos‐de‐Araújo et al. (2024) conducted a cross-sectional in chronic obstructive pulmonary disease (COPD) (n=81). Kubios software corrective filters vs. Other filters was evaluated on HRV-derived parameters in the time domain, frequency domain, and non-linear analysis (p=<0.05). Application of different Kubios filters, especially strong and very strong filters, impacted the quantification of HRV-derived parameters in COPD patients.
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