Intelligent QRS typification using fuzzy clustering

Author

Kweon, H.J. ; Suk, J.W. ; Song, J.S. ; Lee, M.H.

Author_Institution

Dept. of Electr. Eng., Yonsei Univ., Seoul, South Korea

Volume

1

fYear

1995

fDate

20-25 Sep 1995

Firstpage

199

Abstract

For an automated ECG interpretation system, accurate QRS detection and typification including alignment, labelling, and dominant beat selection are essential for enhancing its performance. In this work we present a fuzzy clustering algorithm for intelligent beat labelling to decide whether each of the QRS complexes can be classified as the same cluster or not. Also for reliable classification, we have proposed efficiency feature sets that can best describe the morphology of the QRS complexes

Keywords

electrocardiography; feature extraction; fuzzy logic; mathematical morphology; medical expert systems; medical signal processing; pattern classification; QRS complexes; accurate QRS detection; alignment; automated ECG interpretation system; cluster; dominant beat selection; efficiency feature sets; fuzzy clustering; fuzzy clustering algorithm; intelligent QRS typification; intelligent beat labelling; labelling; morphology; reliable classification; Clustering algorithms; Digital filters; Electrocardiography; Instruments; Labeling; Medical diagnostic imaging; Morphology; Muscles; Rhythm; Signal analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference

Conference_Location

Montreal, Que.

Print_ISBN

0-7803-2475-7

Type

conf

DOI

10.1109/IEMBS.1995.575069

Filename

575069