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- Title
Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques.
- Authors
Malhotra, Vikas; Sandhu, Mandeep Kaur
- 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.
- Subjects
ELECTROCARDIOGRAPHY; HEART disease diagnosis; PSYCHOLOGICAL stress; MACHINE learning; PARTICLE swarm optimization
- Publication
EAI Endorsed Transactions on Scalable Information Systems, 2021, Vol 8, Issue 32, p1
- ISSN
2032-9407
- Publication type
Article
- DOI
10.4108/eai.6-4-2021.169175