Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
Machine learning and artificial intelligence applications to epilepsy: a review for the practicing epileptologist
WT Kerr, KN McFarlane - Current Neurology and Neuroscience Reports, 2023 - Springer
Abstract Purpose of Review Machine Learning (ML) and Artificial Intelligence (AI) are data-
driven techniques to translate raw data into applicable and interpretable insights that can …
driven techniques to translate raw data into applicable and interpretable insights that can …
[HTML][HTML] Vision transformers in image restoration: A survey
The Vision Transformer (ViT) architecture has been remarkably successful in image
restoration. For a while, Convolutional Neural Networks (CNN) predominated in most …
restoration. For a while, Convolutional Neural Networks (CNN) predominated in most …
[HTML][HTML] Temporal and spatial analysis of alzheimer's disease based on an improved convolutional neural network and a resting-state FMRI brain functional network
H Sun, A Wang, S He - … Journal of Environmental Research and Public …, 2022 - mdpi.com
Most current research on Alzheimer's disease (AD) is based on transverse measurements.
Given the nature of neurodegeneration in AD progression, observing longitudinal changes …
Given the nature of neurodegeneration in AD progression, observing longitudinal changes …
[HTML][HTML] Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches
S Mekruksavanich, A Jitpattanakul - Machine Learning and Knowledge …, 2023 - mdpi.com
Epileptic seizures are a prevalent neurological condition that impacts a considerable portion
of the global population. Timely and precise identification can result in as many as 70% of …
of the global population. Timely and precise identification can result in as many as 70% of …
[HTML][HTML] Effective early detection of epileptic seizures through EEG signals using classification algorithms based on t-distributed stochastic neighbor embedding and K …
Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An
electroencephalogram (EEG) can detect seizures as it contains physiological information of …
electroencephalogram (EEG) can detect seizures as it contains physiological information of …
[HTML][HTML] Brain-computer interface prototype to support upper limb rehabilitation processes in the human body
D Camargo-Vargas, M Callejas-Cuervo… - International Journal of …, 2023 - Springer
The high potential for creating brain-computer interfaces (BCIs) and video games for upper
limb rehabilitation has been demonstrated in recent years. In this work, we describe the …
limb rehabilitation has been demonstrated in recent years. In this work, we describe the …
Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using DWT based analysis
C Fıçıcı, Z Telatar, O Eroğul - Biomedical Signal Processing and Control, 2022 - Elsevier
Psychogenic nonepileptic seizure (PNES) and epileptic seizure resemble each other,
behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus …
behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus …
Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time-Frequency Features of EEG Data.
A Akan, HS Ture - International Journal of Neural Systems, 2023 - europepmc.org
The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic
causes, but because their symptoms resemble those of epilepsy, they are frequently …
causes, but because their symptoms resemble those of epilepsy, they are frequently …
[HTML][HTML] Applying multiple functional connectivity features in GCN for EEG-based human identification
W Tian, M Li, X Ju, Y Liu - Brain Sciences, 2022 - mdpi.com
EEG-based human identification has gained a wide range of attention due to the further
increase in demand for security. How to improve the accuracy of the human identification …
increase in demand for security. How to improve the accuracy of the human identification …