Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques
DOI:
https://doi.org/10.4108/eai.6-4-2021.169175Keywords:
Electrocardiogram (ECG) signals, Genetic Algorithm (GA), Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO) and Variational Mode DecompositionAbstract
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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