<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Heart rate variability (HRV) is an important dynamic variable of the cardiovascular system, which operates on multiple time scales. In this study, Multiscale entropy (MSE) analysis is applied to HRV signals taken from Physiobank to discriminate Congestive Heart Failure (CHF) patients from healthy young and elderly subjects. The discrimination power of the MSE method is decreased as the amount of the data reduces and the lowest amount of the data at which there is a clear discrimination between CHF and normal subjects is found to be 4000 samples. Further, this method failed to discriminate CHF from healthy elderly subjects. In view of this, the Reduced Data Dualscale Entropy Analysis method is proposed to reduce the data size required (as low as 500 samples) for clearly discriminating the CHF patients from young and elderly subjects with only two scales. Further, an easy to interpret index is derived using this new approach for the diagnosis of CHF. This index shows 100 % accuracy and correlates well with the pathophysiology of heart failure.</p></abstract>

Heart rate variability (HRV) is an important dynamic variable of the cardiovascular system, which operates on multiple time scales. In this study, Multiscale entropy (MSE) analysis is applied to HRV signals taken from Physiobank to discriminate Congestive Heart Failure (CHF) patients from healthy young and elderly subjects. The discrimination power of the MSE method is decreased as the amount of the data reduces and the lowest amount of the data at which there is a clear discrimination between CHF and normal subjects is found to be 4000 samples. Further, this method failed to discriminate CHF from healthy elderly subjects. In view of this, the Reduced Data Dualscale Entropy Analysis method is proposed to reduce the data size required (as low as 500 samples) for clearly discriminating the CHF patients from young and elderly subjects with only two scales. Further, an easy to interpret index is derived using this new approach for the diagnosis of CHF. This index shows 100 % accuracy and correlates well with the pathophysiology of heart failure.

Reduced Data Dualscale Entropy Analysis of HRV Signals for Improved Congestive Heart Failure Detection

Department of Physics, National Institute of Technology, Warangal-506004, INDIA

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Heart rate variability (HRV) is an important dynamic variable of the cardiovascular system, which operates on multiple time scales. In this study, Multiscale...

Reduced Data Dualscale Entropy Analysis of HRV Signals for Improved Congestive Heart Failure Detection

Published Online: Nov 05, 2014

Page range: 294 - 301

Received: Mar 17, 2014

Accepted: Sep 30, 2014

DOI: https://doi.org/10.2478/msr-2014-0040

Keywords
Multiscale entropy analysis, empirical mode decomposition, heart rate variability, congestive heart failure

© by Srinivas Kuntamalla

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Reduced Data Dualscale Entropy Analysis of HRV Signals for Improved Congestive Heart Failure Detection

Published Online: Nov 05, 2014

Page range: 294 - 301

Received: Mar 17, 2014

Accepted: Sep 30, 2014

DOI: https://doi.org/10.2478/msr-2014-0040

KeywordsMultiscale entropy analysis, empirical mode decomposition, heart rate variability, congestive heart failure

© by Srinivas Kuntamalla

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Multiscale entropy analysis, empirical mode decomposition, heart rate variability, congestive heart failure