Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large numbe...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2002-07, Vol.49 (7), p.733-736
Main Authors: Owis, M.I., Abou-Zied, A.H., Youssef, A.-B.M., Kadah, Y.M.
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmia
Arrhythmias, Cardiac - classification
Arrhythmias, Cardiac - diagnosis
Biological and medical sciences
Biomedical engineering
Chaos
Classification
Correlation
Databases, Factual
Discrimination
Electrocardiography
Electrocardiography - classification
Electrocardiography - methods
Electrocardiography - statistics & numerical data
Feature extraction
Heart
Humans
Lyapunov exponents
Mathematical models
Medical sciences
Models, Cardiovascular
Nonlinear dynamical systems
Nonlinear Dynamics
Patient monitoring
Rhythm
Sensitivity and Specificity
Signal analysis
Signal Processing, Computer-Assisted
Statistical analysis
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Summary:We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2002.1010858