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 classi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Expert systems with applications Jg. 31; H. 1; S. 199 - 205
Hauptverfasser: Güven, Ayşegül, Kara, Sadık
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2006
Schlagworte:
ANN
EOG
Subnormal eye
Subnormal eye
EOG
ANN
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2005.09.017