Fractals in the neurosciences, part II: clinical applications and future perspectives
It has been ascertained that the human brain is a complex system studied at multiple scales,
from neurons and microcircuits to macronetworks. The brain is characterized by a …
from neurons and microcircuits to macronetworks. The brain is characterized by a …
Epileptic seizure detection based on EEG signals and CNN
M Zhou, C Tian, R Cao, B Wang, Y Niu, T Hu… - Frontiers in …, 2018 - frontiersin.org
Epilepsy is a neurological disorder that affects approximately fifty million people according to
the World Health Organization. While electroencephalography (EEG) plays important roles …
the World Health Organization. While electroencephalography (EEG) plays important roles …
An automated system for epilepsy detection using EEG brain signals based on deep learning approach
Epilepsy is a life-threatening and challenging neurological disorder, which is affecting a
large number of people all over the world. For its detection, encephalography (EEG) is a …
large number of people all over the world. For its detection, encephalography (EEG) is a …
Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection …
This study proposes a new model which is fully specified for automated seizure onset
detection and seizure onset prediction based on electroencephalography (EEG) …
detection and seizure onset prediction based on electroencephalography (EEG) …
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 …
Focal and non-focal epilepsy localization: A review
The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which
has affected million people in the world. Hence, an early detection of the focal epileptic …
has affected million people in the world. Hence, an early detection of the focal epileptic …
EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques
Analysis of electroencephalogram (EEG) signals is essential because it is an efficient
method to diagnose neurological brain disorders. In this work, a single system is developed …
method to diagnose neurological brain disorders. In this work, a single system is developed …
A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning …
Epilepsy, a common neurological disorder, is generally detected by electroencephalogram
(EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming …
(EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming …
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 …
Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques
The epilepsies are a heterogeneous group of neurological disorders and syndromes
characterised by recurrent, involuntary, paroxysmal seizure activity, which is often …
characterised by recurrent, involuntary, paroxysmal seizure activity, which is often …