Classification of electro-oculogram signals using artificial neural network

This research is concentrated on the diagnosis of subnormal eye through the analysis of Electrooculography (EOG) signals with the help of Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented. The designed classification structure has about 94.1% sensitivity, 93.3% specifity and positive prediction is calculated to be 94.1%. The end results are classified as normal and subnormal eye. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The benefit of the system is to assist the physician to make the final decision without hesitation. With the future evolution of this system tested on a more populated subject groups, there is always potential for on-line implementation as an auxiliary diagnostic tool on the Electrophysiology machines.