DocumentCode
3435531
Title
A comparison of neural network models for wheeze detection
Author
Forkheim, Kevin E. ; Scuse, David ; Pasterkamp, Hans
Author_Institution
Dept. of Comput. Sci., Manitoba Univ., Winnipeg, Man., Canada
Volume
1
fYear
1995
fDate
15-16 May 1995
Firstpage
214
Abstract
An analysis of the use of neural networks to process lung sounds and identify wheezes is presented. Both raw signal data and Fourier transform data were used to train and test a series of neural networks. The purpose of this study was to compare the performance of the neural networks and their ability to detect wheezes in isolated lung sound segments
Keywords
Fourier transforms; backpropagation; bioacoustics; feedforward neural nets; lung; medical signal processing; patient diagnosis; pneumodynamics; self-organising feature maps; vector quantisation; Fourier transform data; asthma; backpropagation network; isolated lung sound segments; learning vector quantization network; lung sounds; neural network models; performance; radial basis function network; raw signal data; self-organising map network; wheeze detection; Acoustic noise; Computer science; Diseases; Fourier transforms; Frequency; Lungs; Microphones; Neural networks; Performance analysis; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
WESCANEX 95. Communications, Power, and Computing. Conference Proceedings., IEEE
Conference_Location
Winnipeg, Man.
Print_ISBN
0-7803-2725-X
Type
conf
DOI
10.1109/WESCAN.1995.493973
Filename
493973
Link To Document