A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations.
OBJECTIVE
To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events.


METHODS
A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB).


RESULTS
The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%); the best specificity result were attained by ANNB with 95.65%.


CONCLUSIONS
The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.
Carvalho
Lucimar M F de
LM
Passo Fundo University, Passo Fundo, RS, Brazil.
Nassar
Silvia Modesto
SM
Azevedo
Fernando Mendes de
FM
Carvalho
Hugo José Teixeira de
HJ
Monteiro
Lucas Lese
LL
Rech
Ciciliana M Zílio
CM
D000465:Algorithms; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008959:Models, Neurological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity