Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Entropy-Based Data Mining on the Example of Cardiac Arrhythmia Suppression
verfasst von : Martin Bachler, Matthias Hörtenhuber, Christopher Mayer, Andreas Holzinger, Siegfried Wassertheurer
Erschienen in: Brain Informatics and Health
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Heart rate variability (HRV) is the variation of the time interval between consecutive heartbeats and depends on the extrinsic regulation of the heart rate. It can be quantified using nonlinear methods such as entropy measures, which determine the irregularity of the time intervals.
In this work, approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) were used to assess the effects of three different cardiac arrhythmia suppressing drugs on the HRV after a myocardial infarction.
The results show that the ability of all four entropy measures to distinguish between pre- and post-treatment HRV data is highly significant (
p
< 0.01). Furthermore, approximate entropy and sample entropy are able to differentiate significantly (
p
< 0.05) between the tested arrhythmia suppressing agents.