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A pattern recognition approach based on electrodermal response for pathological mood identification in bipolar disorders

机译：基于皮肤电反应的模式识别方法用于双相情感障碍的病理性情绪识别

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This paper reports on results of a pattern recognition technique for classifying pathological mental states of bipolar disorders using information gathered from the electrodermal response. The rationale behind this work is that the autonomic nervous system dynamics, non-invasively quantified through the electrodermal response processing, is altered by the specific mood state. Starting from the hypothesis that bipolar disorders are associated with affective dysfunctions, we processed data gathered from four bipolar patients through eleven experimental trials while an ad-hoc emotional stimulation is administered. Intra- and inter-subject variability were investigated. We show that, using a deconvolution-based approach to estimate sympathetic ANS markers and simple k-Nearest Neighbor algorithms, the proposed methodology is able to discern up to three mood states such as depression, hypo-mania, and euthymia with an average intra-subject accuracy greater than 98% and inter-subject accuracy greater than 82%.
机译：本文报告了一种模式识别技术的结果，该技术使用从皮肤电反应中收集的信息对双相情感障碍的病理性心理状态进行分类。这项工作的基本原理是，通过皮肤电反应过程无创地量化的自主神经系统动力学会因特定的情绪状态而改变。从双相情感障碍与情感功能障碍相关的假设开始，我们通过十一项实验性试验处理了四名双相情感障碍患者收集的数据，同时进行了临时情感刺激。研究对象间和对象间的变异性。我们表明，使用基于反卷积的方法来估计有同感的ANS标记和简单的k最近邻算法，所提出的方法能够辨别多达三种情绪状态，例如抑郁，躁狂和胸膜炎，平均受试者的准确度大于98％，受试者间的准确度大于82％。

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