Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques

Authors

DOI:

https://doi.org/10.4108/eai.6-4-2021.169175

Keywords:

Electrocardiogram (ECG) signals, Genetic Algorithm (GA), Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO) and Variational Mode Decomposition

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.

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Published

06-04-2021

How to Cite

1.
Malhotra V, Kaur Sandhu M. Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques. EAI Endorsed Scal Inf Syst [Internet]. 2021 Apr. 6 [cited 2024 Nov. 13];8(32):e3. Available from: https://publications.eai.eu/index.php/sis/article/view/2054