Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis-Reference-Cited by-同舟云学术

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Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis

Published:2020-02 Issue: Volume:135 Page:109464
ISSN:0306-9877
Container-title:Medical Hypotheses
language:en
Short-container-title:Medical Hypotheses

Author:

Açıkoğlu Merve,Tuncer Seda Arslan

Publisher

Elsevier BV

Subject

General Medicine

Reference38 articles.

1. Yenidoğan Konvülziyonları;Yıldız;Çocuk Dergisi,2013

2. EEG-based neonatal seizure detection with Support Vector Machines;Temko;Clinical Neurophysiol,2011

3. Neonatal seizures and electrographic analysis: evaluation and outcomes;Wical;Pediatr Neurol,1994

4. The role of electroencephalogram in neonatal seizure detection;Francesco;Expert Rev Neurother,2017

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1. Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis;IRBM;2024-08

2. Towards Total Dynamicity of a Feature Selection Method based on the Pareto Front;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection;International Journal of Neural Systems;2024-05-14

4. Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning;Frontiers in Cellular and Infection Microbiology;2023-12-19

5. Design of a Multi-Feature Classification Scheme for Infant Epileptic Seizures;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24

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