Generative adversarial network and convolutional neural network-based EEG imbalanced classification model for seizure detection
B Gao, J Zhou, Y Yang, J Chi, Q Yuan - Biocybernetics and Biomedical …, 2022 - Elsevier
Automatic seizure detection technology is of great significance to reduce workloads of
neurologists for epilepsy diagnosis and treatments. Imbalanced classification is a challenge …
neurologists for epilepsy diagnosis and treatments. Imbalanced classification is a challenge …
A seizure detection method based on hypergraph features and machine learning
X Gao, Y Zhu, Y Yang, F Zhang, F Zhou, X Tian… - … Signal Processing and …, 2022 - Elsevier
Effective feature extraction is the key to successful seizure detection. A good feature should
be adaptable, noise-resistant, and informative. In this study, a seizure detection method …
be adaptable, noise-resistant, and informative. In this study, a seizure detection method …
A multi representation deep learning approach for epileptic seizure detection
Epileptic seizures, unpredictable in nature and potentially dangerous during activities like
driving, pose significant risks to individual and public safety. Traditional diagnostic methods …
driving, pose significant risks to individual and public safety. Traditional diagnostic methods …
Novel ML-Based Algorithm for Detecting Seizures from Single-Channel EEG
YM Dweiri, TK Al-Omary - NeuroSci, 2024 - mdpi.com
There is a need for seizure classification based on EEG signals that can be implemented
with a portable device for in-home continuous minoring of epilepsy. In this study, we …
with a portable device for in-home continuous minoring of epilepsy. In this study, we …
SmartEEG: An End-to-End Framework for the Analysis and Classification of EEG signals
A Ciurea, CP Manoila, B Ionescu - … International Conference on …, 2021 - ieeexplore.ieee.org
EEG analysis frameworks are scientific tools to make neuroscientists' research less
cumbersome. However, the focus has been on manual feature extraction and signal …
cumbersome. However, the focus has been on manual feature extraction and signal …
[HTML][HTML] 基于双密度双树复小波变换的癫痫发作期自动检测算法
同舟康， 润东左， 岚烽钟， 文静陈， 恒张… - Sheng Wu Yi Xue …, 2021 - ncbi.nlm.nih.gov
正确区分癫痫发作期（ seizure） 与非发作期（ non-seizure） 对癫痫治疗有着重要意义。
本研究以颅内脑电信号（ iEEG） 作为研究对象， 提出了一种基于双密度双树复小波变换（ DD-DT …
本研究以颅内脑电信号（ iEEG） 作为研究对象， 提出了一种基于双密度双树复小波变换（ DD-DT …
An Epileptic Seizure Detection Method from EEG Signals based on a Classifier-Driven Feature Reduction Technique
RN Kamel - 2023 - fount.aucegypt.edu
Epileptic seizure detection can improve the quality of life of epileptic patients, allow for more
accurate medication, and minimize the risk of sudden unexpected death in epilepsy …
accurate medication, and minimize the risk of sudden unexpected death in epilepsy …
Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform
T Kang, R Zuo, L Zhong, W Chen, H Zhang… - Sheng wu yi xue …, 2021 - europepmc.org
正确区分癫痫发作期 (seizure) 与非发作期 (non-seizure) 对癫痫治疗有着重要意义.
本研究以颅内脑电信号 (iEEG) 作为研究对象, 提出了一种基于双密度双树复小波变换 (DD-DT …
本研究以颅内脑电信号 (iEEG) 作为研究对象, 提出了一种基于双密度双树复小波变换 (DD-DT …
[引用][C] Efficient EEG feature learning model combining random convolutional kernel with wavelet scattering for seizure detection
Y Liu, Y Jiang, J Liu, J Li, M Liu, W Nie… - International Journal of …, 2024 - World Scientific
Epileptic is a chronic neurological disorder characterized by transient recurrent episodes of
abnormal electrical discharges in brain neurons1. This disease can present with involuntary …
abnormal electrical discharges in brain neurons1. This disease can present with involuntary …