HRV-GUI: A MATLAB Graphical User Interface for Heart Rate Variability Analysis and Validation Using Human, Rodent, and Clinical Diabetic Gastroparesis Data
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
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), Poincaré short-term variability (SD1), and Poincaré 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.
