ArticleScientific reports2022
A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Bidirectional coupling of physiology and emotions reveals unique speaker-listener dynamics.Social cognitive and affective neuroscience · 2026Article
- Association of body mass index and visceral fat with heart rate variability in university students: a BMI-stratified analysis.Frontiers in physiology · 2026Article
- Continuous bidirectional coupling of heart rate variability and emotions in dyad speaker-listener dynamics reveals unique interpersonal synchronization.Frontiers in systems neuroscience · 2026Article
- Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.Frontiers in network physiology · 2025Article
- Validation and user experience of a dry electrode based Health Patch for heart rate and respiration rate monitoring.Scientific reports · 2024Article
- A pilot study using the LASCA technique to analyze stress using heart rate variability.Lasers in medical science · 2024Article
- Article
- Wearable neurofeedback acceptance model for students' stress and anxiety management in academic settings.PloS one · 2024Article
- Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction.Scientific reports · 2023Article
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5 authors.
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Abstract
Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10
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