Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study
Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders
because it provides brain biomarkers. However, only highly trained doctors can interpret …
because it provides brain biomarkers. However, only highly trained doctors can interpret …
Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review
Epilepsy is a chronic neurological disorder with a comparatively high prevalence rate. It is a
condition characterized by repeated and unprovoked seizures. Seizures are managed with …
condition characterized by repeated and unprovoked seizures. Seizures are managed with …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
A multi-objective evolutionary approach based on graph-in-graph for neural architecture search of convolutional neural networks
With the development of deep learning, the design of an appropriate network structure
becomes fundamental. In recent years, the successful practice of Neural Architecture Search …
becomes fundamental. In recent years, the successful practice of Neural Architecture Search …
Epileptic seizure prediction using deep transformer model
The electroencephalogram (EEG) is the most promising and efficient technique to study
epilepsy and record all the electrical activity going in our brain. Automated screening of …
epilepsy and record all the electrical activity going in our brain. Automated screening of …
Epileptic eeg classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
EEG-Based Seizure detection using linear graph convolution network with focal loss
Y Zhao, C Dong, G Zhang, Y Wang, X Chen… - Computer methods and …, 2021 - Elsevier
Abstract Background and Objectives: Epilepsy is a clinical phenomenon caused by sudden
abnormal and excessive discharge of brain neurons. It affects around 70 million people all …
abnormal and excessive discharge of brain neurons. It affects around 70 million people all …
Epileptic seizure detection by cascading isolation forest-based anomaly screening and EasyEnsemble
The electroencephalogram (EEG), for measuring the electrophysiological activity of the
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …
[HTML][HTML] Seizure prediction in EEG signals using STFT and domain adaptation
P Peng, Y Song, L Yang, H Wei - Frontiers in Neuroscience, 2022 - frontiersin.org
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional approaches commonly collect training and testing samples …
resistant epilepsy. Conventional approaches commonly collect training and testing samples …