Department of Electrical, Biomedical and Computer Engineering, University of Pavia, 27100 Pavia, Italy.
Brain and Behavioral Sciences Department, University of Pavia, 27100 Pavia, Italy.
Sensors (Basel). 2023 Feb 23;23(5):2504. doi: 10.3390/s23052504.
Electrodermal Activity (EDA) has become of great interest in the last several decades, due to the advent of new devices that allow for recording a lot of psychophysiological data for remotely monitoring patients' health. In this work, a novel method of analyzing EDA signals is proposed with the ultimate goal of helping caregivers assess the emotional states of autistic people, such as stress and frustration, which could cause aggression onset. Since many autistic people are non-verbal or suffer from alexithymia, the development of a method able to detect and measure these arousal states could be useful to aid with predicting imminent aggression. Therefore, the main objective of this paper is to classify their emotional states to prevent these crises with proper actions. Several studies were conducted to classify EDA signals, usually employing learning methods, where data augmentation was often performed to countervail the lack of extensive datasets. Differently, in this work, we use a model to generate synthetic data that are employed to train a deep neural network for EDA signal classification. This method is automatic and does not require a separate step for features extraction, as in EDA classification solutions based on machine learning. The network is first trained with synthetic data and then tested on another set of synthetic data, as well as on experimental sequences. In the first case, an accuracy of 96% is reached, which becomes 84% in the second case, thus demonstrating the feasibility of the proposed approach and its high performance.
在过去的几十年中，由于新设备的出现，使得远程监测患者健康的大量生理心理数据得以记录，因此，皮肤电活动（EDA）变得备受关注。在这项工作中，提出了一种分析 EDA 信号的新方法，其最终目标是帮助护理人员评估自闭症患者的情绪状态，例如压力和挫败感，这些情绪可能会导致攻击行为的发生。由于许多自闭症患者无法言语或患有述情障碍，因此开发一种能够检测和衡量这些唤醒状态的方法可能有助于预测即将发生的攻击行为。因此，本文的主要目的是对他们的情绪状态进行分类，以便通过适当的措施预防这些危机。已经进行了几项研究来对 EDA 信号进行分类，通常采用学习方法，其中经常进行数据扩充以弥补数据集不足的问题。与此不同，在这项工作中，我们使用模型生成合成数据，然后将其用于训练用于 EDA 信号分类的深度神经网络。该方法是自动的，不需要像基于机器学习的 EDA 分类解决方案那样进行单独的特征提取步骤。首先，该网络在合成数据上进行训练，然后在另一组合成数据以及实验序列上进行测试。在第一种情况下，达到了 96%的准确率，在第二种情况下，准确率为 84%，从而证明了所提出方法的可行性和高性能。
