A novel EEG-complexity-based feature and its application on the epileptic seizure detection

SL Zhang, B Zhang, YL Su, JL Song - International Journal of Machine …, 2019 - Springer
The neurophysiology system is a complex network of nerves and cells, which carries
messages to and from the brain and spinal cord to various parts of the body. Exploring …

Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine

JL Song, W Hu, R Zhang - Neurocomputing, 2016 - Elsevier
Automated seizure detection using EEG has gained increasing attraction in recent years and
appeared more and more helpful in both diagnosis and treatment. How to design an …

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Y Song, J Crowcroft, J Zhang - Journal of neuroscience methods, 2012 - Elsevier
Epilepsy is one of the most common neurological disorders–approximately one in every 100
people worldwide are suffering from it. The electroencephalogram (EEG) is the most …

Automatic seizure detection using a novel EEG feature based on nonlinear complexity

JL Song, R Zhang - 2016 international joint conference on …, 2016 - ieeexplore.ieee.org
Epileptic seizure detection using EEGs is a heavy workload of traditional visual inspection
for diagnosing epilepsy. Therefore, more and more research on automatic seizure detection …

A sequential method using multiplicative extreme learning machine for epileptic seizure detection

D Li, Q Xie, Q Jin, K Hirasawa - Neurocomputing, 2016 - Elsevier
Epilepsy, one of the most common neurological disorders of the human brain, is
unpredictable and irregular. There is much difficulty involved in its detection. Here, a …

Hybrid WCA–PSO Optimized Ensemble Extreme Learning Machine and Wavelet Transform for Detection and Classification of Epileptic Seizure from EEG Signals

S Panda, S Mishra, MN Mohanty - Augmented Human Research, 2023 - Springer
Epilepsy seizures are sudden, chaotic neurological functions. The complexity of the brain is
revealed via electroencephalography (EEG). Visual examination-based EEG signal analysis …

[HTML][HTML] A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine

Y Song, P Liò - Journal of Biomedical Science and Engineering, 2010 - scirp.org
The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy.
Substantial data is generated by the EEG recordings of ambulatory recording systems, and …

[HTML][HTML] Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition

X Zhang, S Dong, Q Shen, J Zhou… - Frontiers in …, 2023 - frontiersin.org
Introduction Intelligent recognition of electroencephalogram (EEG) signals can remarkably
improve the accuracy of epileptic seizure prediction, which is essential for epileptic …

[HTML][HTML] Epileptic seizure detection based on imbalanced classification and wavelet packet transform

Q Yuan, W Zhou, L Zhang, F Zhang, F Xu, Y Leng… - seizure, 2017 - Elsevier
Purpose Automatic seizure detection is significant for the diagnosis of epilepsy and the
reduction of massive workload for reviewing continuous EEG recordings. Methods …

A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection

LL Chen, J Zhang, JZ Zou, CJ Zhao… - … Signal Processing and …, 2014 - Elsevier
Background Many investigations based on nonlinear methods have been carried out for the
research of seizure detection. However, some of these nonlinear measures cannot achieve …