1 research outputs found
Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
Our study concerns with automated predicting of congestive heart failure
(CHF) through the analysis of electrocardiography (ECG) signals. A novel
machine learning approach, regularized hessenberg decomposition based extreme
learning machine (R-HessELM), and feature models; squared, circled, inclined
and grid entropy measurement were introduced and used for prediction of CHF.
This study proved that inclined entropy measurements features well represent
characteristics of ECG signals and together with R-HessELM approach overall
accuracy of 98.49% was achieved.Comment: 9 pages, 3 figures, neuroprocessing lette