Chen, J. D. | bioRxiv Science

bioRxiv2026

A Temporal Reliability and Sustainability Score Framework for Ranking Heart Rate Variability Metrics During Pharmacological Autonomic Blockade in Rats

Background: While Heart Rate Variability (HRV) is a cornerstone of non-invasive autonomic assessment, the sensitivity and sustainability of specific HRV parameters remain debated. This study introduces a "sustainability score" to systematically validate which metrics best reflect autonomic nervous system (ANS) modulation following pharmacological blockade. Methods: Ten male Sprague-Dawley rats were implanted with ECG electrodes. After recovery, rats underwent randomized atropine (1 mg/kg, intraperitoneal) and propranolol (10 mg/kg, intraperitoneal) sessions separated by at least 3 days. Baseline ECG was recorded for 30 min before each session, followed by 90-min post-drug ECG recording. HRV parameters were analyzed in sequential 15-min and 30-min segments. Reliability was assessed using average p-values across post-drug segments and a sustainability score, defined as the number of segments significantly different from baseline. Results: After atropine, HF Power showed the strongest rapid parasympathetic-related response in 15-min segments, with the lowest average p-value (0.1476) and highest sustainability score (4), followed by RSA (p=0.16148; score=4). HFn was less reliable in 15-min segments (p=0.23147; score=2) but became the most reliable parasympathetic-related metric in 30-min segments (p=0.0001; score=3). After propranolol, SI was the most reliable sympathetic-related metric in both 15-min (p=0.18225; score=2) and 30-min segments (p=0.0393; score=2), while SD2/SD1 showed the strongest 30-min sympathovagal-balance response (p=0.0200; score=3). Conclusion: HF Power is recommended for rapid parasympathetic-related assessment in short windows, HFn for longer-window parasympathetic assessment, SI for sympathetic-assessment, and SD2/SD1 for 30-min sympathovagal-balance analysis. Sustainability scoring provides a structured framework for selecting ECG-derived autonomic markers.

pharmacology and toxicology↗

bioRxiv2026

HRV-GUI: A MATLAB Graphical User Interface for Heart Rate Variability Analysis and Validation Using Human, Rodent, and Clinical Diabetic Gastroparesis Data

Background and Objective: Heart rate variability (HRV) analysis provides a non-invasive method for quantifying autonomic modulation from electrocardiographic recordings. However, practical HRV analysis often depends on fragmented workflows, limited signal-quality review, and software tools optimized for either human or preclinical recordings, but not both. This study developed and evaluated HRV-GUI, a MATLAB-based graphical interface for electrocardiogram (ECG)-derived HRV analysis in translational biomedical research. Methods: The HRV-GUI integrates electrocardiographic and RR interval loading, human and rat analysis modes, preprocessing, segment selection, automated R-peak detection, manual peak correction, RR interval generation, multi-domain HRV computation, diagnostic visualization, result export, and session saving/loading. The software was evaluated using deterministic synthetic RR interval datasets, baseline recordings from healthy human controls and healthy rats, and a clinical use-case comparison between healthy controls and patients with diabetic gastroparesis. Results: The HRV-GUI produced expected outputs in synthetic RR validation tests, including constant RR sequences, alternating RR sequences, outlier-containing RR sequences, and low-frequency- or high-frequency-dominant sinusoidal RR modulation. The software generated physiologically plausible HRV profiles in both human and rat recordings. In the clinical use-case analysis, patients with diabetic gastroparesis showed higher heart rate and sympathetic index, together with lower respiratory sinus arrhythmia, absolute low- and high-frequency spectral power, standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), percentage of successive RR intervals differing by more than 50 ms (pNN50), Poincare short-term variability (SD1), and Poincare long-term variability (SD2) compared with healthy controls. Conclusions: HRV-GUI provides an integrated biomedical software workflow for ECG-derived HRV analysis. The validation results support its use for controlled RR testing, human and rodent ECG recordings, and clinical autonomic assessment in diabetic gastroparesis.

bioengineering↗