Fuzzy classification of heart rate trends and artifacts

Author

Sittig, Dean F. ; Cheung, Kei-Hoi ; Berman, Lewis

Author_Institution

Dept. of Biomed. Eng., Vanderbilt Univ., Nashville, TN, USA

fYear

1992

fDate

14-17 Jun 1992

Firstpage

510

Lastpage

519

Abstract

Fuzzy set theory makes it possible to map inexact data, concepts, and events to fuzzy sets via user-defined membership functions. The authors describe a method for (1) robustly estimating the mean and slope of an arbitrary number of data points, (2) developing a set of fuzzy membership functions to classify various properties of heart rate trends, and (3) finding the longest consecutive sequence of heart rate data that fit a particular fuzzy membership function. Preliminary results indicate that fuzzy set theory has significant potential in the development of a clinically robust method for classifying heart rate data, trends, and artifacts

Keywords

cardiology; fuzzy set theory; medical diagnostic computing; patient diagnosis; pattern recognition; artifacts; fuzzy membership functions; fuzzy sets; heart rate trends; user-defined membership functions; Biomedical engineering; Biomedical informatics; Biomedical signal processing; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Heart rate; Robustness; Signal processing; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on

Conference_Location

Durham, NC

Print_ISBN

0-8186-2742-5

Type

conf

DOI

10.1109/CBMS.1992.245009

Filename

245009