[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
P Boonyakitanont, A Lek-Uthai, K Chomtho… - … Signal Processing and …, 2020 - Elsevier
Since the manual detection of electrographic seizures in continuous electroencephalogram
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
Automated detection of schizophrenia using nonlinear signal processing methods
Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to
predict abnormality and cerebral activities. The purpose of this study was to develop an …
predict abnormality and cerebral activities. The purpose of this study was to develop an …
Automated depression detection using deep representation and sequence learning with EEG signals
Depression affects large number of people across the world today and it is considered as
the global problem. It is a mood disorder which can be detected using …
the global problem. It is a mood disorder which can be detected using …
A deep convolutional neural network model for automated identification of abnormal EEG signals
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual
examination of these signals by experts is strenuous and time consuming. Hence, machine …
examination of these signals by experts is strenuous and time consuming. Hence, machine …
Parkinson's disease: Cause factors, measurable indicators, and early diagnosis
S Bhat, UR Acharya, Y Hagiwara, N Dadmehr… - Computers in biology …, 2018 - Elsevier
Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …
Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals
HT Tor, CP Ooi, NSJ Lim-Ashworth, JKE Wei… - Computer Methods and …, 2021 - Elsevier
Background and objectives Attention deficit hyperactivity disorder (ADHD) is often presented
with conduct disorder (CD). There is currently no objective laboratory test or diagnostic …
with conduct disorder (CD). There is currently no objective laboratory test or diagnostic …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
apnea may last for a few seconds and happen for many while sleeping. This reduction in …