An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
AI-based epileptic seizure detection and prediction in internet of healthcare things: a systematic review
Epilepsy is a neurological condition affecting around 50 million individuals worldwide,
reported by the World Health Organization. This is identified as a hypersensitive disease by …
reported by the World Health Organization. This is identified as a hypersensitive disease by …
Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network
The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the
epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the …
epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the …
基于SVM 分类器的癫痫脑电时空特征提取方法的研究.
易芳吉， 钟丽莎， 李章勇 - … of Chongqing University of Posts & …, 2022 - search.ebscohost.com
癫痫发作具有突发性和反复性ꎬ 给患者的生命安全带来巨大隐患ꎮ 为了给患者提供有效的预警
ꎬ 结合时间和空间两个维度ꎬ 选取模糊熵和皮尔逊相关性作为特征参数ꎬ …
ꎬ 结合时间和空间两个维度ꎬ 选取模糊熵和皮尔逊相关性作为特征参数ꎬ …
Multi-view cross-subject seizure detection with information bottleneck attribution
Y Zhao, G Zhang, Y Zhang, T Xiao… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Significant progress has been witnessed in within-subject seizure detection from
electroencephalography (EEG) signals. Consequently, more and more works have been …
electroencephalography (EEG) signals. Consequently, more and more works have been …
Application of deep learning and WT-SST in localization of epileptogenic zone using epileptic EEG signals
Focal and non-focal Electroencephalogram (EEG) signals have proved to be effective
techniques for identifying areas in the brain that are affected by epileptic seizures, known as …
techniques for identifying areas in the brain that are affected by epileptic seizures, known as …
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has
been a common yet lifesaving practice in the Neonatal Intensive Care Unit (NICU). However …
been a common yet lifesaving practice in the Neonatal Intensive Care Unit (NICU). However …
Saga: sparse adversarial attack on EEG-based brain computer interface
With the recent advancement of the Brain-Computer Interface (BCI), Electroencephalogram
(EEG) analytics gain a lot of research attention from various domains. Understanding the …
(EEG) analytics gain a lot of research attention from various domains. Understanding the …
Hardware acceleration of high sensitivity power-aware epileptic seizure detection system using dynamic partial reconfiguration
In this paper, a high-sensitivity low-cost power-aware Support Vector Machine (SVM)
training and classification based system, is hardware implemented for a neural seizure …
training and classification based system, is hardware implemented for a neural seizure …
Hypercomplex multimodal emotion recognition from EEG and peripheral physiological signals
E Lopez, E Chiarantano, E Grassucci… - … , Speech, and Signal …, 2023 - ieeexplore.ieee.org
Multimodal emotion recognition from physiological signals is receiving an increasing
amount of attention due to the impossibility to control them at will unlike behavioral …
amount of attention due to the impossibility to control them at will unlike behavioral …