HRV Studio的验证：一个用于心率变异性分析的透明且感知质量控制的平台
Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis
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中文总结 AI 辅助
本研究开发了开源HRV Studio平台，经多维度验证其在HRV分析中与NeuroKit2、Kubios等工具一致性高、计算稳健，为HRV研究提供了透明可重复的分析平台。
中文摘要 AI 辅助
心率变异性（HRV）分析的可重复性受到不同软件平台预处理和计算惯例差异的限制。我们开发了HRV Studio，这是一个基于PyQt6的开源桌面应用，集成了透明的HRV分析与自动化质量控制（QC）诊断。验证内容包括：与NeuroKit2的大规模一致性分析、针对性的Kubios基准测试、频谱方法比较、合成扰动测试、记录时长敏感性分析，以及聚焦心律失常的QC压力测试。在匹配条件下，HRV Studio在广泛使用的时域指标RMSSD和SDNN上显示出近乎完全一致的结果。在主要的5分钟NeuroKit2对比中，频域指标的中位数相对误差为：低频（LF）1.35%，高频（HF）0.18%，LF/HF为1.41%，而极低频（VLF）受惯例影响更大（37.79%）。非线性庞加莱（Poincaré）指标也表现出高度一致性。序列协调的Kubios基准测试确认了时域和非线性指标的近乎完全一致，以及大多数频域指标的强一致性。扩展的10分钟分析再现了相同的总体模式，部分受惯例影响的频谱输出分歧更低。合成和心律失常压力测试保持了100%的数值稳定性，并持续触发QC警告。总体而言，HRV Studio为HRV研究提供了一个透明且可重复的平台，当NN序列、预处理和分析惯例协调时，具有很强的跨平台一致性。压力测试结果表明其计算稳健性，而非临床验证。
英文摘要
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.
