Accurate Stress Assessment based on functional Near Infrared Spectroscopy using Deep Learning Approach

• URL: http://arxiv.org/abs/2002.06282v1
• Date: Fri, 14 Feb 2020 23:55:08 GMT
• Title: Accurate Stress Assessment based on functional Near Infrared Spectroscopy using Deep Learning Approach
• Authors: Mahya Mirbagheri, Ata Jodeiri, Naser Hakimi, Vahid Zakeri, Seyed Kamaledin Setarehdan
• Abstract summary: In this study, signals produced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recorded from 10 healthy volunteers are employed to assess the stress induced by the Montreal Imaging Stress Task. Experiment results showed that the trained fNIRS model performs stress classification by achieving 88.52 -+ 0.77% accuracy. Its low computational cost opens up the possibility to be applied in real-time stress assessment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, signals produced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recorded from 10 healthy volunteers are employed to assess the stress induced by the Montreal Imaging Stress Task by means of a deep learning system. The proposed deep learning system consists of two main parts: First, the one-dimensional convolutional neural network is employed to build informative feature maps. Then, a stack of deep fully connected layers is used to predict the stress existence probability. Experiment results showed that the trained fNIRS model performs stress classification by achieving 88.52 -+ 0.77% accuracy. Employment of the proposed deep learning system trained on the fNIRS measurements leads to higher stress classification accuracy than the existing methods proposed in fNIRS studies in which the same experimental procedure has been employed. 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 stress assessment.

Related papers

• Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals [0.22499166814992436]
  Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram signals. By incorporating Continuous Wavelet Transformation (CWT) with the proven VGG16, our method enhances stress assessment accuracy and reliability.
  arXiv  Detail & Related papers  (2024-10-17T19:29:52Z)
• Stress Assessment with Convolutional Neural Network Using PPG Signals [0.22499166814992436]
  This research is focused on developing a novel technique to assess stressful events using raw PPG signals recorded by Empatica E4 sensor. An adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD)
  arXiv  Detail & Related papers  (2024-10-16T06:24:16Z)
• Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters [39.96015789655091]
  In many fMRI studies, respiratory signals are often missing or of poor quality. It could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices. This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals.
  arXiv  Detail & Related papers  (2024-04-30T21:53:11Z)
• Event-Driven Learning for Spiking Neural Networks [43.17286932151372]
  Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing. It remains an open challenge how to effectively benefit from the sparse event-driven property of SNNs to minimize backpropagation learning costs. We introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.
  arXiv  Detail & Related papers  (2024-03-01T04:17:59Z)
• A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate [0.0]
  Driver stress is a major cause of car accidents and death worldwide. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions.
  arXiv  Detail & Related papers  (2023-02-03T08:54:55Z)
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference [59.040209568168436]
  We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomHMXP waveform models.
  arXiv  Detail & Related papers  (2022-10-11T18:00:02Z)
• PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression [21.254400561280296]
  Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity. We develop Physiological Multitask-Learning (PhysioMTL) by harnessing Optimal Transport theory within a Multitask-learning framework.
  arXiv  Detail & Related papers  (2022-03-19T19:14:25Z)
• Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks [68.8204255655161]
  Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks. We explore the diversity of the neurons within the hidden layer during the learning process. We analyze how the diversity of the neurons affects predictions of the model.
  arXiv  Detail & Related papers  (2021-09-20T15:12:16Z)
• Personalized Stress Monitoring using Wearable Sensors in Everyday Settings [9.621481727547215]
  We explore objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling.
  arXiv  Detail & Related papers  (2021-07-31T04:15:15Z)
• Predictive Coding Can Do Exact Backpropagation on Any Neural Network [40.51949948934705]
  We generalize (IL and) Z-IL by directly defining them on computational graphs. This is the first biologically plausible algorithm that is shown to be equivalent to BP in the way of updating parameters on any neural network.
  arXiv  Detail & Related papers  (2021-03-08T11:52:51Z)
• Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization [42.125394498649015]
  Spiking Neural Networks (SNNs) capture some of the efficiency of biological brains by processing through binary neural dynamic activations. This paper proposes to leverage multiple compartments that sample independent spiking signals while sharing synaptic weights. The key idea is to use these signals to obtain more accurate statistical estimates of the log-likelihood training criterion, as well as of its gradient.
  arXiv  Detail & Related papers  (2021-02-05T16:39:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.