Abstract：
:In this work, a framework is provided for identifying intracranial electroencephalography (iEEG) seizures based on discrete wavelet transform (DWT) analysis of iEEG signals using forward propagation and feedback neural networks. The performance of 5 different data sets combination classifications is studied using the probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and Elman neural network (ENN). Different feature combinations serve as the input vectors of the classifiers to obtain the best outcomes. It has been found that PNN has less running time and provides better classification accuracy (CA) than ENN and LVQ classifiers for all 5 classification problems. It is worth noticing that the CA for the C-D classification task, which shows the status of pre-ictal versus post-ictal, has been greatly improved, and reached 83.13%. Hence, the epilepsy iEEG signals pattern recognition based on DWT statistical features using the PNN classifier is more suitable for forming a reliable, automatic classification system in order to assist doctors in diagnosis.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Gong C,Zhang X,Niu Ydoi
10.1016/j.compbiolchem.2020.107310subject
Has Abstractpub_date
2020-06-19 00:00:00pages
107310eissn
1476-9271issn
1476-928Xpii
S1476-9271(20)30580-6journal_volume
87pub_type
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