Wickramasuriya Dilranjan S, Faghih Rose T
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:599-602. doi: 10.1109/EMBC.2019.8857917.
Determining the relationship between neurocognitive stress and changes in physiological signals is an important aspect of wearable monitoring. We present a state-space approach for tracking stress from skin conductance and electrocardiography measurements. Individual skin conductance responses (SCRs) are a primary source of information in a skin conductance signal and their rate of occurrence is related to psychological arousal. Likewise, heart rate too varies with emotion. We model SCRs and heartbeats as two different stress-related point processes linked to the same sympathetic nervous system activation. We derive Kalman-like filter equations for tracking stress and use both expectation-maximization and maximum likelihood estimation for parameter recovery. Our preliminary results show that stress is high when a task is unfamiliar, but reduces gradually with familiarity, albeit in the presence of other external stressors. The method demonstrates the feasibility of tracking real-world stress using skin conductance and heart rate measurements. It also serves as a novel state estimation framework for multiple point process observations on different time scales.
确定神经认知应激与生理信号变化之间的关系是可穿戴监测的一个重要方面。我们提出了一种状态空间方法，用于从皮肤电导率和心电图测量中追踪应激。个体皮肤电导率反应（SCR）是皮肤电导率信号中的主要信息来源，其出现频率与心理唤醒有关。同样，心率也会随情绪变化。我们将SCR和心跳建模为与同一交感神经系统激活相关的两个不同的应激相关点过程。我们推导了用于追踪应激的类似卡尔曼滤波器方程，并使用期望最大化和最大似然估计进行参数恢复。我们的初步结果表明，当任务不熟悉时应激水平较高，但随着熟悉程度的提高会逐渐降低，尽管存在其他外部应激源。该方法证明了使用皮肤电导率和心率测量来追踪现实世界应激的可行性。它还为不同时间尺度上的多点过程观测提供了一个新颖的状态估计框架。
