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A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning

机译:一种混合滤波算法,用于帕夫洛维亚恐惧调节的皮肤电导和心率测量的交感神经唤醒跟踪

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Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.
机译:病理恐惧和焦虑症可以对个体患者和社会的影响可能具有衰弱的影响。众所周知,神经电路恐惧学习和灭绝的潜在恐惧学习和灭绝在焦虑症的发展和维持方面发挥着至关重要的作用。 Pavlovian调节,其中一个受试者在生物学相关刺激和中立刺激和中性提示之间学习关联,这一直是引导治疗焦虑症的疗法的发展。迄今为止,在Pavlovian恐惧调理实验中已经分析了许多生理信号响应,例如皮肤电导,心率,脑电图和脑血流。然而,经常分别检查生理标记,以深入了解恐惧习得的神经过程。我们提出了一种在恐惧调理期间从生理信号特征跟踪单一脑与与生理信号的同情唤起状态的方法。我们开发了一种概率地将皮肤传导和心率与未观察到的交感神经唤起状态相关的状态空间制定。我们使用期望 - 最大化框架进行状态估计和模型参数恢复。状态估计通过贝叶斯滤波进行。我们评估我们在追踪恐惧调理实验中获得的模拟和实验数据模型。结果模拟数据显示了我们提出的方法来估计未观察到的唤醒状态和恢复模型参数的能力。实验数据的结果与皮肤传导测量一致,并为作为二进制点过程建模的心跳提供良好的拟合。在状态模型内从皮肤电导和心率跟踪唤醒的能力是可以帮助患者护理的可穿戴监视器的发展的重要前兆。焦虑和创伤相关疾病通常伴随着增长的交感神经,本文所述的方法可以在远程监测中寻找治疗目的的临床应用。

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