H
Hojjat Adeli
Researcher at Ohio State University
Publications - 523
Citations - 37111
Hojjat Adeli is an academic researcher from Ohio State University. The author has contributed to research in topics: Artificial neural network & Wavelet. The author has an hindex of 103, co-authored 511 publications receiving 30859 citations. Previous affiliations of Hojjat Adeli include University College of Engineering & Northwestern University.
Papers
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Journal ArticleDOI
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli +6 more
TL;DR: In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
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Analysis of EEG records in an epileptic patient using wavelet transform.
TL;DR: In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records and the capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals.
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Spiking neural networks.
TL;DR: A state-of-the-art review of the development of spiking neurons and SNNs is presented, and insight into their evolution as the third generation neural networks is provided.
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Neural Networks in Civil Engineering: 1989–2000
TL;DR: Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.
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A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy
TL;DR: It is observed that while there may not be significant differences in the values of the parameters obtained from the original EEG, differences may be identified when the parameters are employed in conjunction with specific EEG subbands.