Managing the noisy glaucomatous test data by self organising maps

Author

Liu, Xiaohui ; Cheng, Gongxian ; Wu, John

Author_Institution

Dept. of Comput. Sci., London Univ., UK

Volume

2

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

649

Abstract

One of the main difficulties in obtaining reliable data from patients in glaucomatous tests is the measurement noise caused by the learning effect, inattention, failure of fixation, fatigue, etc. Using Kohonen´s self-organising feature maps, we have developed a computational method to distinguish between the noise and true measurement. This method has been shown to provide a satisfactory way of locating and rejecting noise in the test data, an improvement over conventional statistical methods

Keywords

learning (artificial intelligence); medical signal processing; noise; patient diagnosis; self-organising feature maps; Kohonen learning method; Kohonen self-organising feature maps; measurement noise; medical signal processing; motion sensitivity perimetry; noise rejection; noisy glaucomatous test data management; patient diagnosis; Automatic testing; Cause effect analysis; Data analysis; Failure analysis; Fatigue; Fluctuations; Hospitals; Noise measurement; Psychology; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374252

Filename

374252