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Papers/Regularized HessELM and Inclined Entropy Measurement for C...

Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction

Apdullah Yayık, Yakup Kutlu, Gökhan Altan

2019-07-12Electrocardiography (ECG)BIG-bench Machine Learning
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Abstract

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.

Results

TaskDatasetMetricValueModel
Electrocardiography (ECG)CHF databaseAccuracy98.49Inclined Entropy (R-HessELM)
Electrocardiography (ECG)CHF databasePrecision98.05Inclined Entropy (R-HessELM)
Electrocardiography (ECG)CHF databaseSensitivity98.3Inclined Entropy (R-HessELM)
Medical waveform analysisCHF databaseAccuracy98.49Inclined Entropy (R-HessELM)
Medical waveform analysisCHF databasePrecision98.05Inclined Entropy (R-HessELM)
Medical waveform analysisCHF databaseSensitivity98.3Inclined Entropy (R-HessELM)

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