Hakimi Naser, Jodeiri Ata, Mirbagheri Mahya, Setarehdan S Kamaledin
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, the Netherlands; Artinis Medical Systems B.V., Elst, the Netherlands.
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Comput Biol Med. 2020 Jun;121:103810. doi: 10.1016/j.compbiomed.2020.103810. Epub 2020 May 11.
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.
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 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.
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.
压力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来评估或缓解压力，已经进行了大量研究。
在本研究中，提出了一种基于卷积神经网络（CNN）方法来评估由蒙特利尔成像应激任务诱发的压力。所提出的模型在源自功能近红外光谱（fNIRS）的心率信号（称为HRF）上进行训练。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。所提出的深度学习系统由两个主要部分组成，在第一部分中，采用一维卷积神经网络构建信息激活图，然后在第二部分中，使用一堆深度全连接层来预测压力存在概率。此后，将所采用的CNN方法与密集神经网络、支持向量机和随机森林在各种分类指标方面进行比较。
结果清楚地表明了CNN相对于所有其他方法的优越性。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%的准确率，比fNIRS模型获得的准确率高10.09%。
采用在HRF测量上训练的所提出的深度学习系统，与在相同实验过程的现有研究中报告的准确率相比，可实现更高的压力分类准确率。此外，所提出的方法显示出更好的稳定性，预测变化较小。此外，其低计算成本为应用于压力评估的实时监测开辟了可能性。
