| MATHEMATIK UND INFORMATIK Eingang: 23.11.2005 Math/Inf/11/05 Als Manuskript gedruckt Entropy Estimation Methods in HRV Analysis of Patients (2008) | |||||||||||||||||
Abstract | |||||||||||||||||
| Heart rate variability (HRV) is a marker for autonomous activity in the heart. A key application of HRV measures is the stratification of mortality risk after myocardial infarction. The information entropy is a promising measure of HRV. Our hypothesis is that the information entropy of HRV, a non-linear approach, is a suitable measure for this. As a first step, we aimed at evaluating the effect of myocardial infarction on the entropy. Our method was to compare the entropy to standard HRV parameters. Essentially, one multivariate classification rule was generated based on existing HRV measures and one based on existing and new entropy measures. The gain in classification accuracy was then an evaluation criterion. The classification rules were expressed as decision trees. The simplicity and parameter choice of the augmented tree was the second criterion. Additionally, five entropy estimation techniques were compared in terms of estimation accuracy and discrimination strength. A key finding is that the entropy is reduced in patients with myocardial infarction with very high significance. Additionally, a simple threshold of the meanNN-normalised entropy outperforms the best multivariate standardsbased infarct classifier by 5-10%. The statistical and compression-based entropy estimations are with a correlation of>94 % highly consistent and thus reliable. The entropy based on Burrows-Wheeler compression, implemented | |||||||||||||||||
Details der Publikation | |||||||||||||||||
| |||||||||||||||||