ArticleScientific reports2026
Effects of electrocardiogram qrs detection algorithms in heart rate variability metrics.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Accurate detection of the electrocardiogram QRS complexes is essential for computing Heart Rate Variability (HRV) metrics. While many QRS detection algorithms exist, their impact on the accuracy of HRV metrics remains underexplored. This study addresses this gap by assessing how QRS detection errors affect HRV analysis across different algorithms and recording setups. We evaluated eight widely used QRS detectors: pan_tompkins, two_average, swt, christov, hamilton, matched_filter, engzee, and wqrs, using ECG recordings from 25 healthy participants under rest, cognitive load, and physical activity conditions. Two acquisition setups were considered: “chest strap”, which offers the most consistent signal quality in this dataset, and “loose cables”, a configuration more susceptible to artifacts. We used the manually annotated R-peaks to calculate the ground-truth HRV metric values. We evaluated the relationship between detector performance and HRV errors for 23 metrics using the concordance correlation coefficient (CCC), which combines accuracy and precision to quantify agreement with ground-truth HRV values. Results showed significant variability in detector performance across algorithms and setups. No single QRS detection algorithm consistently outperformed the others. Across all metrics, CCC values ranged from 0.99 down to 0 or slightly negative depending on the detector and condition, with loose cable data showing up to 0.10–0.15 higher agreement than chest strap recordings for the most robust metrics. Based on aggregated CCC values across HRV metrics and conditions, the two_average detector achieved the highest overall agreement, whereas engzee showed the lowest performance. These findings highlight the critical role of QRS detector selection and signal acquisition conditions in HRV analysis. They underscore the need for context-specific benchmarking, particularly for wearable and ambulatory applications where signal quality can vary.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.