Malhotra, Vikas and Sandhu, Mandeep Kaur (2021) Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques. EAI Transactions on Scalable Information Systems. e3. ISSN 2032-9407
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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.
Item Type: | Article |
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Uncontrolled Keywords: | Electrocardiogram (ECG) signals, Genetic Algorithm (GA), Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO) and Variational Mode Decomposition |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science QA75 Electronic computers. Computer science T Technology > T Technology (General) |
Depositing User: | EAI Editor IV |
Date Deposited: | 26 Jul 2021 15:31 |
Last Modified: | 26 Jul 2021 15:31 |
URI: | https://eprints.eudl.eu/id/eprint/5178 |