A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals-Reference-Cited by-同舟云学术

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A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals

Published:2021-01 Issue: Volume:163 Page:113788
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Shoeibi Afshin,Ghassemi Navid,Alizadehsani Roohallah,Rouhani Modjtaba,Hosseini-Nejad Hossein,Khosravi Abbas,Panahiazar Maryam,Nahavandi Saeid

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference100 articles.

1. Characterization of focal EEG signals: a review;Acharya;Future Generation Computer Systems,2019

2. Entropies for automated detection of coronary artery disease using ecg signals: A review;Acharya;Biocybernetics and Biomedical Engineering,2018

3. Automated EEG analysis of epilepsy: a review;Acharya;Knowledge-Based Systems,2013

4. Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection;Akyol;Expert Systems with Applications,2020

5. A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification;Al Ghayab;Journal of Neuroscience Methods,2019

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3. Targeting the Receptor Complexes by Structure-based Natural Drug Compounds in Seizures: A New Dimension in Drug Discovery and Design;The Natural Products Journal;2024-06-04

4. Machine Learning for Epilepsy: A Comprehensive Exploration of Novel EEG and MRI Techniques for Seizure Diagnosis;Journal of Medical and Biological Engineering;2024-06

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