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.