Why the study?
Continuous physiological data are increasingly available, yet their value for digital medicine remains limited when temporal context, signal quality, subject identity, and baseline state are not encoded in machine-readable form.
Does an acquisition-agnostic real-time heart rate variability pipeline accurately detect beats, assess signal quality, and process HRV metrics with low latency across benchmark datasets and real-world emergency simulations?
Does an acquisition-agnostic real-time heart rate variability pipeline accurately detect beats, assess signal quality, and process HRV metrics with low latency across benchmark datasets and real-world emergency simulations?
This study establishes a technically validated, low-latency, real-time infrastructure capable of transforming raw RR interval streams from heterogeneous sensors into quality-informed, baseline-referenced, and machine-readable data objects for digital biomarker research.
Answers are grounded in this paper and cite their sources.
May facilitate real-time HRV pipelines for digital biomarkers; extends Hamilton and Orphanidou benchmarks with low-latency validation.
Continuous physiological data are increasingly available, yet their value for digital medicine remains limited when temporal context, signal quality, subject identity, and baseline state are not encoded in machine-readable form. We developed an acquisition-agnostic real-time heart rate variability pipeline that transforms live RR streams into event-synchronized, baseline-referenced, quality-informed physiological data objects for downstream analytics and inference. Validation spanned acquisition, signal quality, processing, real-world feasibility, and latency. Across 109,494 expert-annotated beats from all 48 MIT-BIH Arrhythmia Database records, the embedded ECG-to-RR route achieved an F1 score of 0.9851, comparable to an independent Hamilton benchmark (0.9844). Under controlled ECG degradation, Signal Confidence discriminated expert-defined RR-sequence degradation with an AUROC of 0.754 versus 0.662 for an Orphanidou-based comparator. Real-world feasibility was demonstrated in five dyadic emergency-simulation sessions involving ten physicians, with all recordings technically usable. Median measured latency was 7.99 ms. This establishes a technically validated first-layer infrastructure for inference-ready digital biomarker development.
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Gellisch et al. (2026) studied Heart rate variability monitoring and ECG beat detection (n=10). Acquisition-agnostic real-time heart rate variability (HRV) pipeline with embedded Pan-Tompkins ECG-to-RR detector vs. Hamilton QRS detector and Orphanidou-based ECG signal-quality index was evaluated on Beat detection F1 score across 109,494 expert-annotated beats in the MIT-BIH Arrhythmia Database.
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