C-GRASP：基于临床的感知信号处理推理
C-GRASP: Clinically-Grounded Reasoning for Affective Signal Processing
AI总结：
C-GRASP通过临床基础推理提升情感信号处理的准确性与临床一致性。
摘要：
心率变异性（HRV）是自主神经监测的重要无创标志物；然而，将大型语言模型（LLMs）应用于HRV解释受到生理幻觉的阻碍。这些幻觉包括呼吸性窦性心律（RSA）污染、非线性指标在短数据时的不稳定性，以及优先使用群体规范而非个性化基线的忽视。我们提出C-GRASP（用于情感信号处理的临床基础推理），一种具有护栏的RAG增强流程，将HRV解释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，强制优先考虑个性化基线偏移而非规范统计。该系统通过自动RSA意识护栏有效缓解频谱幻觉，防止污染频域指数。在DREAMER数据集的414次试验中评估，C-GRASP与大规模推理模型（如MedGemma3-thinking）结合，在4类情感分类（37.3%准确率）和临床推理一致性（CRC）得分69.6%方面表现优异。消融研究证实，个性化Delta Z分数模块是关键的逻辑锚点，防止了原生LLMs中常见的“群体偏见”。最终，C-GRASP将情感计算从黑箱分类转向透明、基于证据的临床决策支持，为生物医学工程中更安全的AI整合铺平了道路。
英文摘要：
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.
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