Nonlinear Dynamical and Entropic Complexity Measures as Indicators of Nonstationarities in Short-Term ECG Signals

Heart rate variability (HRV) is among important characteristics of general cardiac health. While 24-hour Holter monitoring is well recognized as a comprehensive analytical technique, short-term (up to 30 min.) electrocardiogram (ECG), recorded in presence of controlled environmental stimuli, remains...

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Bibliographic Details
Published in2009 IEEE International Conference on Bioinformatics and Biomedicine : 1-4 November 2009 pp. 269 - 278
Main Author Ladysz, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Biomedical monitoring
Cardiology
complexity
compression
Compression algorithms
Doped fiber amplifiers
Electrocardiography
Entropy
Heart rate variability
HRV
Lempel-Ziv
Noise robustness
nonlinear
nonstationary
Sample Entropy
Signal analysis
time series
Time series analysis
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Summary:Heart rate variability (HRV) is among important characteristics of general cardiac health. While 24-hour Holter monitoring is well recognized as a comprehensive analytical technique, short-term (up to 30 min.) electrocardiogram (ECG), recorded in presence of controlled environmental stimuli, remains rather unexplored in diagnostic practice. The presented method of change point detection in such class of signals addresses urgent quest from cardiologists for a simple, real time analysis of short-term ECG. Based on the concepts of sample entropy and Lempel-Ziv compression, the methodology combines sensitivity to subtle dynamical changes of the heart rate with robustness to noise. It was evaluated on 20 ECG signals from two groups of subjects: healthy and with cardiac problems. The method helps improve accuracy in discerning between the two groups and the results support hypothesis that healthy individuals are more responsive to environmental stimuli.
ISBN:0769538851
9780769538853
DOI:10.1109/BIBM.2009.49