An optimization approach to recognition of epileptogenic data using neural networks with simplified input layers.
This study introduces a simplified approach for the implementation of artificial neural networks (ANN) for the recognition of epileptic data in electroencephalograph (EEG) recordings. The training set construction is based on a trend-adaptive polygon which simplifies the search process as it reduces the size of the training set. This data reduction, at a sampling rate of 200 Hz, yielded a reduction ratio of 34% as a minimum to an 81% in the best case scenario. With a higher sampling rate of 500 Hz, a reduction ratio of 73% as a minimum to an impressive 92% in the best case scenario was achieved. The outcome is thus a computationally attractive classifier with a simpler design implementation and with higher prospects for accurate diagnosis. The algorithm was trained and tested with EEG data from four epileptic patients using the k-fold cross-validation technique.
Ayala
Melvin
M
Center for Advanced Technology and Education, Department of Electrical & Computer Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL 33174, USA. Melvin.Ayala@fiu.edu
Adjouadi
Malek
M
Yaylali
Ilker
I
Jayakar
Prasanna
P
D000200:Action Potentials; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology; D001931:Brain Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity