当前位置: X-MOL 学术Sensors › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images.
Sensors ( IF 3.9 ) Pub Date : 2020-09-17 , DOI: 10.3390/s20185312
Sami Elzeiny 1 , Marwa Qaraqe 1
Affiliation  

Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models.

中文翻译:

使用基于光体积描记图的空间和频域图像进行应力分类。

压力是主观的,在一个人与另一个人之间表现出不同的压力。因此,对压力状态进行分类的通用分类模型的性能很粗糙。建立特定于人的模型会导致可靠的分类,但是它需要收集新数据来为每个人训练新模型,并且由于压力是动态的,因此需要定期升级。在本文中,针对对象的压力状态,提出了一种新的二元分类(称为压力和非压力)方法,其中将从光电体积描记图(PPG)中提取的心跳间隔转移到空间图像,然后转移到频域图像。到连续的数量。然后,使用卷积神经网络(CNN)训练并验证人的压力状态的分类准确性。建立了三种类型的分类模型:个人特定模型,通用分类模型和校准通用分类模型。在训练,验证和测试方面,使用空间图像和频域图像的特定人模型获得的平均分类准确率分别为99.9%,100%和99.8%,以及99.68%,98.97%和96.4%。通过将从测试对象中收集的样本的20%合并到训练数据中,可以提高校准后的通用模型的准确性,并且在空间和频域图像上均优于通用性能。训练集,验证集和测试集的平均分类准确率分别为99.6%,99.9%和88.1%,99.2%,97.4%和87.6%,对一系列心跳间隔(IBI)空间和频域图像使用校准的基于通用分类的方法。这项研究的主要贡献是使用从PPG信号中提取的IBI的空间域图像生成的频域图像,通过构建特定于人的模型和经过校准的通用模型来对个体的压力状态进行分类。
更新日期:2020-09-18
down
wechat
bug