Predictability in heartbeat data

Ahmet Ugur, Aydin Cecen

Research output: Contribution to journalConference articlepeer-review


Predicting the behavior of chaotic dynamical systems is difficult in general. It is important to study such systems since the existence of chaos implies potential short term predictability. Several methods exist to analyze time series, including correlation dimension and the Brock-Dechert-Scheinkman- LeBaron (BDSL) test. Recently, a new tool, sample entropy (SampEn), has gained importance for data differentiation. We have applied these methods to cardiovascular time series data. Our findings suggest that correlation dimension is useful in analyzing such data, but not of sufficient power to discriminate between various data generating processes while sample entropy can be used as a supplementary tool.

Original languageEnglish
Pages (from-to)187-191
Number of pages5
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
StatePublished - 2005
EventIEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, United States
Duration: Oct 10 2005Oct 12 2005


  • Chaos
  • Correlation dimension
  • Heartbeat data analysis
  • Sample entropy


Dive into the research topics of 'Predictability in heartbeat data'. Together they form a unique fingerprint.

Cite this