ECG feature relevance in a fuzzy arrhythmia classifier

Author

Silipo, R. ; Zong, W. ; Berthold, M.

Author_Institution

Int. Comput. Sci. Inst., Berkeley, CA, USA

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

679

Lastpage

682

Abstract

A good fuzzy classifier for a three-class arrhythmia problem is analyzed “a posteriori”, in order to estimate the importance of the ECG features for the final system performance. A set of fuzzy rules is automatically built on forty MIT-BIH database´s files, using fourteen ECG measures to characterize each beat. The implemented fuzzy model correctly classifies 92% of normal beats, 80% of PVBs and 56% of SVPBs of the test set. The information contained in the fuzzy model is then quantified and the gain in information, derived from application of the fuzzy rules along a given input dimension, is measured. Only three ECG features (QRS width, PR segment and prematurity degree) seem to carry the main responsibility for the correct classification of the three arrhythmic classes. The uninformative character of ECG measures with lowest information gain is confirmed by the almost unaltered-when not improved-performance of the classifier when these features are removed from the input vector

Keywords

electrocardiography; feature extraction; fuzzy logic; medical signal processing; pattern classification; ECG feature relevance; MIT-BIH database files; PR segment; PVB; QRS width; SVPB; fuzzy arrhythmia classifier; fuzzy rules; input vector; normal beats; prematurity degree; three-class arrhythmia problem; Biomedical measurements; Computer science; Data analysis; Electrocardiography; Fuzzy sets; Fuzzy systems; Gain measurement; Performance analysis; Spatial databases; System performance;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Cardiology, 1999

Conference_Location

Hannover

ISSN

0276-6547

Print_ISBN

0-7803-5614-4

Type

conf

DOI

10.1109/CIC.1999.826062

Filename

826062