Low HRV entropy is strongly associated with myocardial infarction.
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.
Lau
Stephan
S
Institute of Computer Science, Friedrich Schiller University Jena, Jena, Germany.
Haueisen
Jens
J
Schukat-Talamazzini
Ernst G
EG
Voss
Andreas
A
Goernig
Matthias
M
Leder
Uwe
U
Figulla
Hans-R
HR
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology*; D001683:Biological Clocks; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004562:Electrocardiography / Q000379:methods*; D019277:Entropy; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology*; D009991:Oscillometry / Q000379:methods; D013223:Statistics as Topic