Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6854962 | Expert Systems with Applications | 2018 | 16 Pages |
Abstract
Experimental results show that the proposed ECG signal representation using sparse decomposition technique with PSO optimized least-square twin SVM (best classifier model among k-NN, PNN and RBFNN) reported higher classification accuracy of 99.11% in category and 89.93% in personalized schemes respectively than the existing methods to the state-of-art diagnosis.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Sandeep Raj, Kailash Chandra Ray,