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Our study concerns with automated predicting of congestive heart failure(CHF) through the analysis of electrocardiography (ECG) signals. A novelmachine learning approach, regularized hessenberg decomposition based extremelearning machine (R-HessELM), and feature models; squared, circled, inclinedand grid entropy measurement were introduced and used for prediction of CHF.This study proved that inclined entropy measurements features well representcharacteristics of ECG signals and together with R-HessELM approach overallaccuracy of 98.49% was achieved.

Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction