Computers in Biology and Medicine 35 (2005) 565–582 http://www.intl.elsevierhealth.com/journals/cobm A mixture of experts network structure for modelling Doppler ultrasound blood ow signals Inan G uler * , Elif Derya  Ubeyl˙  Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey Received 12 January 2004; accepted 13 April 2004 Abstract Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illus- trates the use of ME network structure to guide modelling Doppler ultrasound blood ow signals. Expectation –Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that ts well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. ? 2004 Elsevier Ltd. All rights reserved. Keywords: Mixture of experts; Expectation–Maximization algorithm; Diagnostic accuracy; Discrete wavelet transform; Doppler signal; Ophthalmic artery; Internal carotid artery 1. Introduction There have recently been widespread interests in the use of multiple models for pattern classi- cation and regression in statistics and neural network communities. The basic idea underlying these methods is the application of a so-called divide-and-conquer principle that is often used to tackle a complex problem by dividing it into simpler problems whose solutions can be combined to yield * Corresponding author. Tel.: +90-312-212-3976; fax: +90-312-212-0059. E-mail address: iguler@gazi.edu.tr (I. G uler). 0010-4825/$-see front matter ? 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.compbiomed.2004.04.001