Abstract
Heart rate variability (HRV) is a marker of autonomous activity in the heart. An important application of HRV measures is the stratification of mortality risk after myocardial infarction. Our hypothesis is that the information entropy of HRV, a non-linear approach, is a suitable measure for this assessment. As a first step, to evaluate the effect of myocardial infarction on the entropy, we compared the entropy to standard HRV parameters. The entropy was estimated by compressing the tachogram with Bzip2. For univariate comparison, statistical tests were used. Multivariate analysis was carried out using automatically generated decision trees. The classification rate and the simplicity of the decision trees were the two evaluation criteria. The findings support our hypothesis. The meanNN-normalized entropy is reduced in patients with myocardial infarction with very high significance. One entropy parameter alone exceeds the discrimination strength of multivariate standards-based trees.
References
1 Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol1987; 59: 256–262.10.1016/0002-9149(87)90795-8Search in Google Scholar
2 Task Force of the European Society of Cardiology, the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Eur Heart J 1996; 17: 354–381.Search in Google Scholar
3 Voss A, Hnatkova K, Wessel N, et al. Multiparametric analysis of heart rate variability used for risk stratification among survivors of acute myocardial infarction. Pace1998; 21: 186–192.10.1111/j.1540-8159.1998.tb01086.xSearch in Google Scholar PubMed
4 Bell TC, Cleary JG, Witten IH. Text compression. Advanced reference series on computer science. Englewood Cliffs: Prentice Hall 1990.Search in Google Scholar
5 Burrows M, Wheeler DJ. A block-sorting lossless data compression algorithm. Technical Report 124. Palo Alto: Digital Systems Research Center 1994.Search in Google Scholar
6 Seward J. Bzip2 and libbzip2 – a program and library for data compression. Manual. 2001. Available at: www.bzip.org.Search in Google Scholar
7 Breiman L. Classification and regression trees. New York: Chapman & Hall 1998.Search in Google Scholar
8 Baumert M, Baier V, Haueisen J, et al. Forecasting of life threatening arrhythmias using the compression entropy of heart rate. Method Inform Med2004; 43: 202–206.10.1055/s-0038-1633859Search in Google Scholar
9 Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol2000; 278: 2039–2049.10.1152/ajpheart.2000.278.6.H2039Search in Google Scholar PubMed
10 Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev Lett2005; 71: 021906.10.1103/PhysRevE.71.021906Search in Google Scholar PubMed
©2006 by Walter de Gruyter Berlin New York