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

Author

Jovic, Alan ; Bogunovic, Nikola

Author_Institution

Rudjer Boskovic Inst., Zagreb

fYear

2007

fDate

25-28 June 2007

Firstpage

63

Lastpage

68

Abstract

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.

Keywords

chaos; correlation methods; data mining; electrocardiography; entropy; feature extraction; medical signal processing; time series; ECG chaos extractor; ECG time-series mining; approximate entropy; attractors; central tendency measure; correlation dimension; feature extraction; phase space; spatial filling index; Biological system modeling; Chaos; Electrocardiography; Extraterrestrial measurements; Feature extraction; Heart; Laboratories; Principal component analysis; Psychology; Statistical analysis; ECG analysis; chaos theory; feature extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology Interfaces, 2007. ITI 2007. 29th International Conference on

Conference_Location

Cavtat

ISSN

1330-1012

Print_ISBN

953-7138-10-0

Electronic_ISBN

1330-1012

Type

conf

DOI

10.1109/ITI.2007.4283745

Filename

4283745