Mental Stress Assessment Using PPG Signal a Deep Neural Network Approach

Kalra, Prerita; Sharma, Vivek

doi:10.1080/03772063.2020.1844068

Cited by 28 publications

(9 citation statements)

References 21 publications

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“…They achieve 94.4% accuracy using an SVM BRF classifier. Prerita Kalra and Vivek Sharma [77] use 18 characteristics of the PPG signal to identify a possible stress episode, 9 of them from the time domain and the remaining 9 from the frequency domain. With a DNN, they reach 91% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

et al. 2023

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Stress is one of the primary triggers of serious pathologies (e.g., depression, obesity, heart attack). Prolonged exposure to it can lead to addictive substance consumption and even suicide, without ignoring other adverse side effects in the economic, work and family spheres. Early detection of stress would relax the pressure of medical practice exercised by the population affected and result in a healthier society with a more satisfying quality of life. In this work, a convolutional-neural-network (CNN) model is proposed to detect an individual’s stress state by analyzing the diffusive dynamics of the photoplethysmographic (PPG) signal. The characteristic (p,q)-planes of the 0–1 test serve as a framework to preprocess the PPG signals and feed the CNN with the dynamic information they supply to typify an individual’s stress level. The methodology follows CRISP-DM (Cross Industry Standard Process for Data Mining), which provides the typical steps in developing data-mining models. An adaptation of CRIPS-DM is applied, adding specific transitions between the usual stages of deep-learning models. The result is a CNN model whose performance amounts to 97% accuracy in diagnosing the stress level; it compares with other published results.

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Section: Discussionmentioning

confidence: 99%

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

et al. 2023

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“…[16]. In addition, a handful of researches [17] are also available that uses only PPG signal features for the estimation of the mental stress. The requirement of multisensory acquisition for multimodal signal analysis enhances the methodological complexity and the acquisition burden.…”

Section: Introductionmentioning

confidence: 99%

Mental stress detection using multimodal characterization of PPG signal for personal healthcare applications

Paul¹,

Chakraborty

Sadhukhan

et al. 2023

Preprint

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With growing complexities in our society, mental stress has become inevitable in every human life. Long-term mental stress condition could instigate several chronic diseases and thus require its early evaluation. Existing mental stress estimation techniques mostly uses complicated, multi-channel and expert dependent electroencephalogram (EEG) based approaches. Moreover, the respiratory signal presents promising stress-related information, but its acquisition is also complicated and needs multimodal assistance. Hence, in this research a unique approach based on multimodal characterization of the easy-to-acquire Photoplethysmogram (PPG) signal is proposed to assess the stressed condition. Exclusively, the developed algorithm not only uses a primary PPG feature, but also derives the respiratory rate from the same PPG signal via simplified methodologies. The technique is evaluated on the PPG recordings collected from the publicly available DEAP dataset. Efficiency of these easy-to-compute features is then assessed via a simple threshold-based classification technique to categorize the stressed and the relaxed conditions with an average accuracy of 98.43%. Compared to the existing methods, the proposed algorithm not only shows improved performance but the associated simple methodology with minimum acquisition load also justifies its applicability in real-time standalone, personal healthcare applications.

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“…Among these works, Charlton et al [11] present a numerical study to explore the influence of the time domain features of a simulated pulse wave on mental stress. In another study by Kalra and Sharma [12], PPG along with a deep learning technique was used to detect five levels of cognitive mental stress, yielding an Ac of 91%. Granitto et al [13] used a single PPG signal for binary and multiclass classification with an Ac of 96.56% and 78%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Real-time mental stress detection technique using neural networks towards a wearable health monitor

Mukherjee

Mukhopadhyay

Gupta

2022

Meas. Sci. Technol.

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Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

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Mental Stress Assessment Using PPG Signal a Deep Neural Network Approach

Cited by 28 publications

References 21 publications

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

Mental stress detection using multimodal characterization of PPG signal for personal healthcare applications

Real-time mental stress detection technique using neural networks towards a wearable health monitor

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