Open AccessIntracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier
Author(s) -
Chen GONG,
Jiahui LIU,
Yunyun NIU
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.03.005
Subject(s) - ictal , pattern recognition (psychology) , artificial intelligence , epileptic seizure , artificial neural network , computer science , entropy (arrow of time) , binary classification , feature extraction , sample entropy , approximate entropy , classifier (uml) , epilepsy , support vector machine , neuroscience , psychology , physics , quantum mechanics
Automatic identification of intracranial electroencephalogram (iEEG) signals has become more and more important in the field of medical diagnostics. In this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of iEEG epileptic seizures. Four kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network (NN) during the identification process. Four kinds of classification tasks, namely Pre‐ictal v Post‐ictal (CD), Pre‐ictal v Epileptic (CE), Post‐ictal v Epileptic (DE), Pre‐ictal v Post‐ictal v Epileptic (CDE), are used to test the effect of our classification method. The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms. The effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.