  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.
