Background

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

Method

In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics.

Results

Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model.

Conclusions

Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.

中文翻译：

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提出了一种卷积神经网络，用于通过功能近红外光谱法导出心率来进行压力评估。

背景

压力被认为是威胁人类健康的主要因素之一。为了通过分析大脑和心脏相关信号评估或缓解压力，已进行了大量研究。

方法

在这项研究中，提出了一种基于卷积神经网络（CNN）方法的方法来评估由蒙特利尔成像压力任务诱发的压力。在从功能近红外光谱（fNIRS）（称为HRF）得出的心率信号上训练提出的模型。在这方面，使用位于前额叶皮层的23个通道的配置记录了20名健康志愿者的fNIRS信号。拟议的深度学习系统包括两个主要部分，其中第一部分使用一维卷积神经网络构建信息激活图，然后在第二部分中使用一堆深度完全连接的层来预测压力存在概率。此后，将采用的CNN方法与密集神经网络，支持向量机，

结果

结果清楚地表明了CNN优于所有其他方法。此外，训练后的HRF模型明显优于在滤波后的fNIRS信号上训练的模型，其中HRF模型可以达到98.69±0.45％的精度，比fNIRS模型获得的精度高10.09％。

结论

使用在HRF测量结果上训练的拟议深度学习系统可以比在已完成相同实验程序的现有研究中报告的准确性更高的压力分类准确性。此外，所提出的方法提出了更好的稳定性和较低的预测变化。此外，其低的计算成本为在压力评估的实时监控中应用提供了可能性。