Hybrid fuzzy-neural committee networks for recognition of swallow acceleration signals

Author

Reddy, Narender P. ; Das, Amitava ; Simcox, Denise

Author_Institution

Dept. of Biomed. Eng., Akron Univ., OH, USA

Volume

3

fYear

1998

fDate

29 Oct-1 Nov 1998

Firstpage

1375

Abstract

Swallowing gives rise to characteristic patterns of acceleration in normal and dysphagic individuals. Usually, there are several artifacts present in the signal due to speech, coughing, etc. In the present study, two sets of fuzzy-neural committee networks were developed and trained to recognized acceleration signals due to swallowing. Evaluation showed that the networks well recognized the swallow signals and artifacts

Keywords

acceleration measurement; biomedical measurement; feature extraction; fuzzy neural nets; medical expert systems; medical signal processing; pattern classification; signal classification; artifacts recognition; characteristic patterns of acceleration; dysphagic individuals; feature extraction; fuzzified parameters; hybrid fuzzy-neural committee networks; hybrid intelligent system; signal classification; swallow acceleration signals recognition; swallowing patterns; Acceleration; Accelerometers; Character recognition; Fuzzy logic; Hospitals; Liquids; Neural networks; Pattern recognition; Signal processing; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE

Conference_Location

Hong Kong

ISSN

1094-687X

Print_ISBN

0-7803-5164-9

Type

conf

DOI

10.1109/IEMBS.1998.747136

Filename

747136