HRV Studio实现心率变异性分析的透明化与质量控制，提升结果可复现性。
Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis
- 基于PyQt6开发，集成透明分析流程与自动化质量诊断
- 在匹配条件下，时间域指标与NeuroKit2几乎完全一致，频域误差<1.5%
- 支持多场景测试，适合需高可复现性的生理信号研究者
心率变异性（HRV）分析的可复现性受限于不同软件平台间预处理和计算惯例的差异。我们开发了HRV Studio，一个基于PyQt6的开源桌面应用，整合透明化HRV分析与自动化质量控制（QC）诊断。验证包括大规模与NeuroKit2的一致性对比、针对Kubios的基准测试、谱方法比较、合成扰动测试、记录时长敏感性分析及心律失常导向的QC压力测试。在五分钟主对比中，时间域指标RMSSD和SDNN与NeuroKit2近乎一致；频域中，LF、HF和LF/HF的中位相对误差分别为1.35%、0.18%和1.41%，而VLF更受惯例影响（37.79%）。非线性Poincaré指数也表现出高度一致性。序列对齐后的Kubios基准测试证实时间域与非线性指标近乎一致，多数频域指标保持强一致性。十分钟后分析显示部分谱输出分歧更低。合成与心律失常压力测试均保持100%数值稳定性并持续触发QC警告。总体而言，当NN序列、预处理和分析惯例统一时，HRV Studio提供了高度可复现的分析平台。压力测试结果表明其计算鲁棒性，而非临床有效性。
原文摘要 · Abstract (English)
Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincaré indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.
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