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Emotional reactivity monitoring using electrodermal activity analysis in individuals with suicidal behaviors

机译：在自杀行为中使用个人电力活动分析的情绪反应性监测

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Suicide, considered as one of the leading causes of death, has not been given enough attention in order to reduce it's rate. The problem addressed in this paper is the analysis of the relation between an extra stimulus and physiological data's responses. In order to record the physiological data set from multiple subjects over many weeks, we used an acoustic startle during a Paced Auditory Serial Addition Task (PASAT) test that spontaneously leads subjects to real emotional reactivity, without any deliberate laboratory setting. Crucially, we show that, by inducing anxiety during the test, changes appear in Electrodermal activity, Electrocardiogram, Heart Rate and Respiration Rate. A wide range of physiological features from various analysis domains, including modeling, time/frequency analysis, an algorithm and etc., is proposed in order to find the best emotional reactivity feature to correlate them with emotional states which can be considered as a suicide factor. More specifically, this paper is focused on the EDA data analysis. Experimental results highlight that all cited techniques perform well and we achieved a high resolution of tonic and phasic components which allow us to measure the latency, onsets and amplitudes of EDA responses to a stimulus. This paper follows the association of recommendations for advancement of health care instruments.
机译：自杀，被认为是死亡的主要原因之一，并没有足够的重视，以减少它的速度。本文解决的问题是分析额外刺激和生理数据的反应之间的关系。为了在多周内从多个受试者记录生理数据，我们在花节目序列加法任务（PASAT）测试期间使用了声学惊吓，其自发地引导受试者进行真正的情绪反应性，没有任何故意的实验室环境。至关重要的是，我们表明，通过在测试过程中诱导焦虑，改变出现在电台活性，心电图，心率和呼吸率。来自各种分析域的广泛的生理特征，包括建模，时间/频率分析，算法等，以找到最佳的情绪反应性特征，以将它们与情感状态相关联，这些功能可以被视为自杀因子。更具体地，本文重点是EDA数据分析。实验结果突出显示所有引用的技术表现良好，我们实现了辅助和相位成分的高分辨率，使我们能够测量EDA对刺激措施的延迟，持续持续量和幅度。本文遵循了卫生保健工具推进建议的协会。

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