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 …
An overview of EEG-based machine learning methods in seizure prediction and opportunities for neurologists in this field
B Maimaiti, H Meng, Y Lv, J Qiu, Z Zhu, Y Xie, Y Li… - Neuroscience, 2022 - Elsevier
The unpredictability of epileptic seizures is one of the most problematic aspects of the field of
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …
Apple leaf disease recognition method with improved residual network
H Yu, X Cheng, C Chen, AA Heidari, J Liu, Z Cai… - Multimedia Tools and …, 2022 - Springer
The occurrence of apple diseases has dramatically affected the quality and yield of apples.
Disease monitoring is an important measure to ensure the healthy development of the apple …
Disease monitoring is an important measure to ensure the healthy development of the apple …
Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation
Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation
L Liu, D Zhao, F Yu, AA Heidari, C Li, J Ouyang… - Computers in biology …, 2021 - Elsevier
This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on
swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a …
swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a …
A temporal-spectral-based squeeze-and-excitation feature fusion network for motor imagery EEG decoding
Motor imagery (MI) electroencephalography (EEG) decoding plays an important role in brain-
computer interface (BCI), which enables motor-disabled patients to communicate with the …
computer interface (BCI), which enables motor-disabled patients to communicate with the …
Corn leaf diseases diagnosis based on K-means clustering and deep learning
H Yu, J Liu, C Chen, AA Heidari, Q Zhang… - IEEE …, 2021 - ieeexplore.ieee.org
Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during
the growth and production stages of corn. In order to address this problem, this paper …
the growth and production stages of corn. In order to address this problem, this paper …
A dual-branch dynamic graph convolution based adaptive transformer feature fusion network for EEG emotion recognition
Electroencephalograph (EEG) emotion recognition plays an important role in the brain-
computer interface (BCI) field. However, most of recent methods adopted shallow graph …
computer interface (BCI) field. However, most of recent methods adopted shallow graph …
Patient-specific seizure prediction via adder network and supervised contrastive learning
Deep learning (DL) methods have been widely used in the field of seizure prediction from
electroencephalogram (EEG) in recent years. However, DL methods usually have numerous …
electroencephalogram (EEG) in recent years. However, DL methods usually have numerous …
Depression recognition from EEG signals using an adaptive channel fusion method via improved focal loss
J Shen, Y Zhang, H Liang, Z Zhao, K Zhu… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Depression is a serious and common psychiatric disease characterized by emotional and
cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression …
cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression …