Research Article
Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques
@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: Online First}, volume={}, number={}, 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
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.
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.