Research on T-wave morphology analysis in ECG signal

Author

Song, Jinzhong ; Yan, Hong ; Xiao, Zhijun

Author_Institution

State Key Lab. of Space Med. Fundamentals & Applic., China Astronaut Res. & Training Center, Beijing, China

Volume

3

fYear

2010

fDate

16-18 Oct. 2010

Firstpage

1033

Lastpage

1036

Abstract

T-wave morphology classification and recognition plays an important role in clinical diagnosis based on Electrocardiogram (ECG). The integrated method based on Principal Component Analysis (PCA), threshold, linear regression, and symbolic method was used to analyze T-wave morphologies in this paper, and it was simple and convenient to implement. All kinds of T-wave shapes, expressed by symbol `ABCDE´, could be included by this method, and the recognition results were obvious, which could provide a reliable scientific basis for the clinical detection. After European ST-T database verification, the accuracy of T-wave morphology analysis was above 92%.

Keywords

electrocardiography; medical signal processing; principal component analysis; regression analysis; ECG signal; PCA; ST-T database; T-wave morphology analysis; classification; electrocardiogram; linear regression; principal component analysis; recognition; Accuracy; Cardiology; Computers; Electrocardiography; Morphology; Principal component analysis; Shape; T-wave; electrocardiogram (ECG); linear regression; morphology analysis; principal component analysis (PCA); symbolic method;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on

Conference_Location

Yantai

Print_ISBN

978-1-4244-6495-1

Type

conf

DOI

10.1109/BMEI.2010.5639740

Filename

5639740