基于基序异常分析的心电信号可解释压力检测
Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis
- Zaven P. and Sonia Akian College of Science & Engineering, American University of Armenia（美国亚美尼亚大学扎文·P.与索尼娅·阿基扬科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出基于基序发现与矩阵轮廓分析的可解释个性化心电压力检测框架，无需训练分类器，通过异常评分检测压力，在WESAD数据集上验证了有效性并揭示个体差异。
中文摘要 AI 辅助
利用生理信号进行压力检测因其对身心健康的影响而受到广泛关注。虽然基于机器学习和深度学习的现有方法取得了较强的预测性能，但它们往往依赖黑盒模型，且未能捕捉生理反应中的个体差异。在本工作中，我们提出了一种基于基序发现和矩阵轮廓分析的可解释且个性化的心电信号压力检测框架。该方法不训练分类器，而是通过提取心电信号中重复出现的心跳模式（基序）来学习受试者特定的基线心脏行为。随后，利用基于距离的异常评分，将压力检测为相对于这些基线模式的偏离。实验在WESAD数据集上进行，采用精心设计的训练-验证-测试协议以确保评估的可靠性。结果表明，所提方法能够有效检测多名受试者的压力，并通过直接比较心电模式提供清晰的解释性。然而，由于生理反应的差异，性能在个体间存在变化，部分受试者在压力下表现出极小的形态变化。结合心率变异性特征进行的额外分析揭示，虽然心率变异性在某些情况下可以提升性能，但其贡献在所有受试者中并不一致。这些发现强调了个性化和可解释性在生理压力检测中的重要性，并表明基于基序的方法为黑盒模型提供了一种有意义的替代方案，同时也揭示了因受试者间差异而存在的固有局限性。
英文摘要
Stress detection using physiological signals has gained significant attention due to its impact on both physical and mental health. While existing approaches based on machine learning and deep learning achieve strong predictive performance, they often rely on black-box models and fail to capture individual variability in physiological responses. In this work, we propose an interpretable and personalized framework for stress detection using electrocardiogram (ECG) signals based on motif discovery and Matrix Profile analysis. Instead of training a classifier, the method learns subject-specific baseline cardiac behavior by extracting recurring heartbeat patterns (motifs) from ECG signals. Stress is then detected as a deviation from these baseline patterns using a distance-based anomaly score. Experiments are conducted on the WESAD dataset using a carefully designed train-validation-test protocol to ensure reliable evaluation. The results show that the proposed approach can effectively detect stress for several subjects while providing clear interpretability through direct comparison of ECG patterns. However, the performance varies across individuals due to differences in physiological responses, with some subjects exhibiting minimal morphological changes under stress. Additional analysis incorporating heart rate variability (HRV) features reveals that while HRV can improve performance in certain cases, its contribution is not consistent across all subjects. These findings highlight the importance of personalization and interpretability in physiological stress detection and demonstrate that motif-based approaches provide a meaningful alternative to black-box models, while also revealing inherent limitations due to inter-subject variability.
