Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images.
Sami Elzeiny,Marwa Qaraqe +1 more
TL;DR: The use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal is used to classify the stress state of the individual by building person-specific models and calibrated generic models.
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Abstract: 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.
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Citations
Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review
TL;DR: This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life and highlights the potential impact of using PPG signals on an individual’s quality of life and public health.
Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.
TL;DR: In this article, a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture was proposed to classify stress signals by analyzing ECG signals and extracting specific parameters.
Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning
TL;DR: Wang et al. as discussed by the authors investigated the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy.
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Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning
Lili Zhu,Petros Spachos,Pai Chet Ng,Yuanhao Yu,Yang Wang,Konstantinos N. Plataniotis,Dimitrios Hatzinakos +6 more
TL;DR: Wang et al. as mentioned in this paper investigated the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy.
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Detecting stress through 2D ECG images using pretrained models, transfer learning and model compression techniques
TL;DR: In this paper , the authors proposed an algorithm to convert 1D (dimensional) ECG data from WESAD (wearable stress and affect detection dataset) into 2D ECG images, which are representative of stress/not stress.
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