| Title | Compression entropy contributes to risk stratification in patients with cardiomyopathy. | ||
| Author | Truebner, Sandra; Cygankiewicz, Iwona; Schroeder, Rico; Baumert, Mathias; Vallverdu, Montserrat; Caminal, Pere; Vazquez, Rafael; Bayes de Luna, Antoni; Voss, Andreas | ||
| Journal | Biomed Tech (Berl) | Publication Year/Month | 2006-Jul |
| PMID | 16915769 | PMCID | -N/A- |
| Affiliation | 1.Department of Medical Engineering, University of Applied Sciences Jena, Jena, Germany. | ||
Sudden cardiac death (SCD) is a leading cause of mortality with an incidence of 3 million cases per year worldwide. Therapies for patients who have survived an SCD episode or have a high risk of developing lethal ventricular arrhythmia are well established and depend mainly on risk stratification. In this study we investigated the suitability of the non-linear measure compression entropy (Hc) for improved risk prediction in cardiac patients. We recorded 24-h Holter ECG for 300 patients with congestive heart failure (CHF). During a mean follow-up period of 12 months, 32 patients died due to a cardiac event. Hc depends on the compression parameters window length w and buffer length b, which were optimised by analysing a subgroup of patients. Compression entropies based on the beat-to-beat interval (BBI) were subsequently calculated and compared with standard heartrate variability parameters. Statistical analysis revealed significant differences between high- and low-risk CHF patients in standard HRV measures, as well as compression entropy based on the BBI (cardiac death, p = 0.005; SCD, p = 0.02). In conclusion, the implementation of non-linear compression entropy analysis in multivariate analysis seems to be useful for enhanced risk stratification of cardiac death, especially SCD, in ischaemic cardiomyopathy patients.