Abstract:
:The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, multiple signal classification (MUSIC), autoregressive (AR) and periodogram methods were used to get power spectra in patients with absence seizure. The EEG power spectra were used as an input to a classifier. We introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression (LR) and the emerging computationally powerful techniques based on artificial neural networks (ANNs). LR as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN-based classifier was more accurate than the LR-based classifier.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Alkan A,Koklukaya E,Subasi Adoi
10.1016/j.jneumeth.2005.04.009subject
Has Abstractpub_date
2005-10-30 00:00:00pages
167-76issue
2eissn
0165-0270issn
1872-678Xpii
S0165-0270(05)00134-2journal_volume
148pub_type
杂志文章abstract:BACKGROUND:Primary cell culture is a valuable tool to utilize in parallel with in vivo studies in order to maximize our understanding of the mechanisms surrounding neurogenesis and central nervous system (CNS) regeneration and plasticity. The zebrafish is an important model for biomedical research and primary neural ce...
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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