Effects of Electrocardiograms QRS Detection Algorithms in Heart Rate Variability Metrics

Heart Rate Variability (HRV) is a marker used for assessing autonomic nervous system function, derived from the timing between R-peaks in electrocardiogram (ECG) signals. Accurate detection of QRS complexes is essential for reliable HRV computation. While many R-wave 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 using ECG recordings from 25 healthy participants under rest, cognitive load, and physical activity conditions. Two acquisition setups were considered: “chest strap” and “loose cables.” We used the manually annotated R-peaks to calculate the ground-truth HRV metric values. The relationship between the detector performance and HRV errors was evaluated for 11 metrics using the concordance correlation coefficient (CCC). Results showed significant variability in detector performance across algorithms and setups. No single QRS detection algorithm outperformed across all scenarios. Loose cable recordings yielded higher CCC values than chest straps, particularly for MeanNN and LF power. 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. Ultimately, this study offers practical recommendations for clinicians and researchers on selecting QRS detection algorithms that best align with their specific analytical objectives and recording conditions.