Heart rate variability (HRV) is a pivotal noninvasive marker for autonomic monitoring; however, applying Large Language Models (LLMs) to HRV interpretation is hindered by physiological hallucinations. These include respiratory sinus arrhythmia (RSA) contamination, short-data instability in nonlinear metrics, and the neglect of individualized baselines in favor of population norms. We propose C-GRASP (Clinically-Grounded Reasoning for Affective Signal Processing), a guardrailed RAG-enhanced pipeline that decomposes HRV interpretation into eight traceable reasoning steps. Central to C-GRASP is a Z-score Priority Hierarchy that enforces the weighting of individualized baseline shifts over normative statistics. The system effectively mitigates spectral hallucinations through automated RSA-aware guardrails, preventing contamination of frequency-domain indices. Evaluated on 414 trials from the DREAMER dataset, C-GRASP integrated with high-scale reasoning models (e.g., MedGemma3-thinking) achieved superior performance in 4-class emotion classification (37.3% accuracy) and a Clinical Reasoning Consistency (CRC) score of 69.6%. Ablation studies confirm that the individualized Delta Z-score module serves as the critical logical anchor, preventing the "population bias" common in native LLMs. Ultimately, C-GRASP transitions affective computing from black-box classification to transparent, evidence-based clinical decision support, paving the way for safer AI integration in biomedical engineering.

翻译：心率变异性（HRV）是自主神经监测的关键无创标志物；然而，将大语言模型（LLMs）应用于HRV解读时，常受生理性幻觉的阻碍。这些幻觉包括呼吸性窦性心律失常（RSA）污染、非线性指标在短数据中的不稳定性，以及忽视个体化基线而偏向群体常模的问题。我们提出了C-GRASP（基于临床依据的情感信号处理推理），这是一个带有防护机制的RAG增强流程，将HRV解读分解为八个可追溯的推理步骤。C-GRASP的核心是一个Z分数优先级层次结构，强制在统计加权中优先考虑个体化基线偏移而非规范性统计量。该系统通过自动化的RSA感知防护机制，有效缓解了频谱幻觉，防止了频域指标的污染。在DREAMER数据集的414次试验中评估，C-GRASP与高规模推理模型（如MedGemma3-thinking）集成后，在4类情绪分类中取得了优异性能（准确率37.3%），临床推理一致性（CRC）得分达到69.6%。消融研究证实，个体化Delta Z分数模块是关键逻辑锚点，防止了原生LLMs中常见的“群体偏差”。最终，C-GRASP将情感计算从黑盒分类转向透明、基于证据的临床决策支持，为生物医学工程中更安全的人工智能集成铺平了道路。