A stability-first dual-Lorentzian HRV analysis pipeline with dual-Lyapunov guarantee: Parametric efficiency, robust deployability and in silico pharmacological validation
Year: 2026
Type: software
Abstract: Lightweight open-source Python toolbox for interpretable heart rate variability (HRV) analysis, constructed based on Lindblad open quantum dissipative dynamics and dual-Lyapunov stability theory. Core advantages of this toolbox: 1. Only 2 physiologically interpretable dynamical biomarkers (γ, κ, |ξ|), 75% parameter reduction compared with 2-State HMM baseline. 2. Up to 117× faster spectral fitting speed, optimized for low-power edge wearable deployment. 3. Built-in manifold physiological constraints to avoid divergent fitting outputs, guarantees 100% valid biomarker extraction from clinical RR sequences. 4. Included reproduce_paper.py to fully replicate the spectral fitting benchmark results in Table 1 of the corresponding submitted manuscript. This software package contains 5 core files: - lindblad_hrv.py: Core algorithm implementation - example.py: Quick demo for single-sample HRV analysis - reproduce_paper.py: Benchmark reproduction script - LICENSE: MIT open-source license text - readme.md: User guide, dependency list and citation template Released under MIT License. This software is the supplementary implementation for the manuscript submitted to Computer Methods and Programs in Biomedicine.
Institution: Al-Farabi Kazakh National University
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7169537052