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sis 21(32): e3

Research Article

Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques

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  • @ARTICLE{10.4108/eai.6-4-2021.169175,
        author={Vikas Malhotra and Mandeep Kaur Sandhu},
        title={Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={8},
        number={32},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={4},
        keywords={Electrocardiogram (ECG) signals, Genetic Algorithm (GA), Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO) and Variational Mode Decomposition},
        doi={10.4108/eai.6-4-2021.169175}
    }
    
  • Vikas Malhotra
    Mandeep Kaur Sandhu
    Year: 2021
    Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques
    SIS
    EAI
    DOI: 10.4108/eai.6-4-2021.169175
Vikas Malhotra1,*, Mandeep Kaur Sandhu1
  • 1: Department of Electronic and Communication Engineering, University School of Engineering and Technology, Rayat Bahra University, Mohali, Punjab, India
*Contact email: malhotrav586@gmail.com

Abstract

INTRODUCTION: ECG have emerged as the most acceptable and widely used technique to infer mental health status using cardiac signals thereby resolving major challenge of Mental Health Assessment protocols.

OBJECTIVES: Authors mainly aimed at identification of stressed signals to distinguish subjects exhibiting stress ECG signals.

METHODS: Authors have taken advantage of three optimization techniques namely, Genetic Algorithm (GA), Artificial Bee Colony (ABC) and improved Particle Swarm Optimization (PSO) that further improves the classification accuracy of Multi-kernel SVM.

RESULTS: The simulation analysis confer that the proposed work outperforms the existing works while demonstrating an average accuracy, precision, recall and specificity of 98.93%, 96.83%, 96.83% and 96.72%, respectively when evaluated against dataset comprising of 1000 ECG samples.

CONCLUSION: It is observed that the proposed stress prediction based on improved VMD and Improved SVM outperformed the existing work that comprised of traditional VMD and SVM.

Keywords
Electrocardiogram (ECG) signals, Genetic Algorithm (GA), Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO) and Variational Mode Decomposition
Received
2020-11-12
Accepted
2021-03-24
Published
2021-04-06
Publisher
EAI
http://dx.doi.org/10.4108/eai.6-4-2021.169175

Copyright © 2021 Vikas Malhotra et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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