ECG Beat Classification Using Mirrored Gauss Model

Author

Zhou, Qunyi ; Liu, Xing ; Duan, Huilong

Author_Institution

Dept. of Inf. Technol. & Electron. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou

fYear

2006

Firstpage

5587

Lastpage

5590

Abstract

Accurate electrocardiogram (ECG) beat classification is essential for automated detection of arrhythmias. A novel classification algorithm of the ECG beats, applying mirrored Gauss model (MGM) had been proposed in this paper. The MGM has strong morphological representation ability for QRS complex waves using curve fitting. With the MGM, the width of QRS complex wave could be extracted and applied to ECG beat classification easily, effectively and automatically. It was proved by experiment carrying out using all of ECG records in MIT-BIH Arrhythmia Database that the MGM is a promising algorithm for ECG beat classification. The whole classification accuracy is 93.93% for normal beats and 93.94% for premature ventricular contraction (PVC) beats

Keywords

curve fitting; electrocardiography; medical signal processing; signal classification; signal representation; ECG beat classification; QRS complex waves; automated arrhythmia detection; curve fitting; electrocardiogram; mirrored Gauss model; morphological representation ability; premature ventricular contraction beats; Biomedical engineering; Curve fitting; Educational institutions; Educational technology; Electrocardiography; Gaussian processes; Hospitals; Information technology; Morphology; Polynomials; Arrhythmias beat classification; Gauss function; curving fitting;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615752

Filename

1615752