[HTML][HTML] A review on extreme learning machine

J Wang, S Lu, SH Wang, YD Zhang - Multimedia Tools and Applications, 2022 - Springer
Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …

A multi-view deep learning framework for EEG seizure detection

Y Yuan, G Xun, K Jia, A Zhang - IEEE journal of biomedical and …, 2018 - ieeexplore.ieee.org
The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …

[HTML][HTML] Minireview of epilepsy detection techniques based on electroencephalogram signals

G Liu, R Xiao, L Xu, J Cai - Frontiers in systems neuroscience, 2021 - frontiersin.org
Epilepsy is one of the most common neurological disorders typically characterized by
recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy …

Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier

M Mursalin, Y Zhang, Y Chen, NV Chawla - Neurocomputing, 2017 - Elsevier
Abstract Analysis of electroencephalogram (EEG) signal is crucial due to its non-stationary
characteristics, which could lead the way to proper detection method for the treatment of …

[图书][B] The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance

PS Addison - 2017 - taylorfrancis.com
This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …

LMD based features for the automatic seizure detection of EEG signals using SVM

T Zhang, W Chen - IEEE Transactions on Neural Systems and …, 2016 - ieeexplore.ieee.org
Achieving the goal of detecting seizure activity automatically using electroencephalogram
(EEG) signals is of great importance and significance for the treatment of epileptic seizures …

Personalized real-time federated learning for epileptic seizure detection

S Baghersalimi, T Teijeiro, D Atienza… - IEEE journal of …, 2021 - ieeexplore.ieee.org
Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by
the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant …

Fuzzy distribution entropy and its application in automated seizure detection technique

T Zhang, W Chen, M Li - Biomedical Signal Processing and Control, 2018 - Elsevier
Visual inspection of Electroencephalogram (EEG) records is the conventional diagnostic
method of epilepsy but it is expensive, time-consuming and tedious. Therefore, it is …

Effective epileptic seizure detection by using level-crossing EEG sampling sub-bands statistical features selection and machine learning for mobile healthcare

SM Qaisar, SF Hussain - Computer Methods and Programs in Biomedicine, 2021 - Elsevier
Mobile healthcare is an emerging approach which can be realized by using cloud-
connected biomedical implants. In this context, a level-crossing sampling and adaptive-rate …

Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest

T Zhang, W Chen, M Li - Biocybernetics and Biomedical Engineering, 2018 - Elsevier
Purpose Visual inspection of electroencephalogram (EEG) records by neurologist is the
main diagnostic method of epilepsy but it is particularly time-consuming and expensive …