A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction

J Zhang, S Zheng, W Chen, G Du, Q Fu, H Jiang - Scientific Reports, 2024 - nature.com
Epilepsy is one of the most well-known neurological disorders globally, leading to
individuals experiencing sudden seizures and significantly impacting their quality of life …

Spatial attention-based residual network for human burn identification and classification

DP Yadav, T Aljrees, D Kumar, A Kumar, KU Singh… - Scientific Reports, 2023 - nature.com
Diagnosing burns in humans has become critical, as early identification can save lives. The
manual process of burn diagnosis is time-consuming and complex, even for experienced …

Automatic Seizure Detection Based on Stockwell Transform and Transformer

X Zhong, G Liu, X Dong, C Li, H Li, H Cui, W Zhou - Sensors, 2023 - mdpi.com
Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the
brain. Seizure detection algorithms are essential in reducing the workload of medical staff …

A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning

X Zhang, X Zhang, Q Huang, F Chen - Frontiers in Neuroscience, 2024 - frontiersin.org
Epilepsy is a chronic neurological disorder that poses significant challenges to patients and
their families. Effective detection and prediction of epilepsy can facilitate patient recovery …

Efficient seizure prediction and EEG channel selection based on multi-objective optimization

R Jana, I Mukherjee - IEEE Access, 2023 - ieeexplore.ieee.org
Epileptic seizures are unpredictable events due to sudden abnormal electrical activities in
the brain of epilepsy patients. A seizure can be predicted by analyzing the EEG signals to …

[HTML][HTML] Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM

KK Palanisamy, A Rengaraj - Brain Sciences, 2024 - mdpi.com
In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic
seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging …

Investigating the variations in the brain activity between healthy subjects and Mild Cognitive Impairment (MCI) patients

N Pakniyat, B Ramakrishnan, V Pallavi, O Krejcar… - Fractals, 2023 - World Scientific
Analysis of brain activity for patients with brain disorders is an important research area. Mild
cognitive impairment (MCI) is a condition in which patients have more memory or thinking …

Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models

X Pan, J Huang, Y Zhang, Z Zuo, L Zhang - Sensors, 2024 - mdpi.com
High-rise building machines (HBMs) play a critical role in the successful construction of
super-high skyscrapers, providing essential support and ensuring safety. The HBM's …

Multi-perspective characterization of seizure prediction based on microstate analysis

W Shi, Y Cao, F Chen, W Tong, L Zhang… - Frontiers in …, 2024 - frontiersin.org
Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the
affected individual's social functionality and quality of life. This study aims to integrate …

A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learning and Deep Learning

M Hosseinzadeh, P Khoshvaght, S Sadeghi… - IEEE …, 2024 - ieeexplore.ieee.org
Epileptic seizures can be dangerous as they cause sudden and uncontrolled electrical
activity in the brain which can lead to injuries if one falls or loss of control over physical …