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Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning.

Nilima Salankar1, Deepika Koundal1, Saeed Mian Qaisar2,3

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This study introduces a noninvasive method for stress recognition using electroencephalogram (EEG) and electrocardiography (ECG) signals. Machine learning models achieved 100% accuracy in identifying stress markers from brain activity.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Mental health awareness is increasing, highlighting the need for effective stress detection methods.
  • Noninvasive physiological signals like electroencephalogram (EEG) and electrocardiography (ECG) offer a cost-effective approach to stress monitoring.
  • Existing methods may lack accuracy or cost-efficiency in real-time stress identification.

Purpose of the Study:

  • To develop and validate an effective approach for recognizing stress markers using multimodal physiological signals.
  • To identify stress at different brain lobes (frontal, temporal, central, occipital) through signal processing.
  • To evaluate the performance of machine learning algorithms for automated stress classification.

Main Methods:

  • Utilized a dataset of EEG+ECG signals from 36 participants during a mental arithmetic task, categorized as stressed ('Bad') and nonstressed ('Good').
  • Applied Variational Mode Decomposition (VMD) for signal preprocessing and decomposition into oscillatory modes.
  • Extracted features from Poincare plots of VMD modes and performed statistical significance testing using the Wilcoxon test.
  • Employed Multilayer Perceptron (MLPN) and Support Vector Machine (SVM) for stress classification.

Main Results:

  • Variational Mode Decomposition effectively processed multimodal physiological signals.
  • Extracted features from Poincare plots showed statistical significance (p < 0.5).
  • MLPN achieved 100% accuracy in stress recognition for frontal and temporal lobes.
  • SVM also demonstrated high classification performance.

Conclusions:

  • The proposed method effectively identifies stress markers from EEG and ECG signals.
  • High accuracy achieved by MLPN suggests its potential for automated stress detection systems.
  • This approach can be integrated into noninvasive, EEG-based systems for real-time stress identification.