ArticleJournal of medical systems2012
Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 32 citations in OpenAlex.
- Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.Annals of neurology · 2025Article
- Motor imagery EEG signal classification with a multivariate time series approach.Biomedical engineering online · 2023Article
- PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.Computational and mathematical methods in medicine · 2020Article
- [Tract-based spatial statistics analysis on the white matter of patients with temporal lobe epilepsy and automatic recognition].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2017Article
- Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.Computers in biology and medicine · 2015Article
- Automated diagnosis of epilepsy using EEG power spectrum.Epilepsia · 2012Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.