Feature Extraction for ECG Time-Series Mining Based on Chaos Theory

Chaos theory applied to ECG feature extraction is presented in this article. Several chaos methods, including phase space and attractors, correlation dimension, spatial filling index, central tendency measure and approximate entropy are explained in detail. A new feature extraction environment called ECG chaos extractor has been created in order to apply these chaos methods. System model and program functions are presented. Some of the obtained results are listed. Future work in this field of research is discussed.