Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain
A widespread brain disorder of present days is depression which influences 264 million of
the world's population. Depression may cause diverse undesirable consequences, including …
the world's population. Depression may cause diverse undesirable consequences, including …
[PDF][PDF] Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO.
Accepted: 13 December 2020 Late detection of depression is having detrimental
consequences including suicide thus there is a serious need for an accurate computer …
consequences including suicide thus there is a serious need for an accurate computer …
Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain
Electroencephalography (EEG) signals are considered one of the oldest techniques for
detecting disorders in medical signal processing. However, brain complexity and the non …
detecting disorders in medical signal processing. However, brain complexity and the non …
Exploiting feature selection and neural network techniques for identification of focal and nonfocal EEG signals in TQWT domain
For drug resistance patients, removal of a portion of the brain as a cause of epileptic
seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy …
seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy …
A novel computer-aided diagnosis framework for EEG-based identification of neural diseases
Recent advances in electroencephalogram (EEG) signal classification have primarily
focused on domain-specific approaches, which impede algorithm cross-discipline capability …
focused on domain-specific approaches, which impede algorithm cross-discipline capability …
Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms
Surgery is recommended for epilepsy diagnosis in cases where patients do not respond well
to anti-epilepsy medications. Successful surgery is essentially dependent on the area …
to anti-epilepsy medications. Successful surgery is essentially dependent on the area …
Classification of focal and non-focal EEG signals using optimal geometrical features derived from a second-order difference plot of FBSE-EWT rhythms
Background/introduction: Manual detection and localization of the brain's epileptogenic
areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An …
areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An …
[HTML][HTML] Epileptic seizure detection using geometric features extracted from sodp shape of eeg signals and asylncpso-ga
R Wang, H Wang, L Shi, C Han, Y Che - Entropy, 2022 - mdpi.com
Epilepsy is a neurological disorder that is characterized by transient and unexpected
electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is …
electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is …
Detection of seizure EEG signals based on reconstructed phase space of rhythms in EWT domain and genetic algorithm
H Akbari, S Saraf Esmaili… - Signal Processing and …, 2020 - spre.stb.iau.ir
Epilepsy is a brain disorder which stems from the abnormal activity of neurons and recording
of seizures has primary interest in the evaluation of epileptic patients. A seizure is the …
of seizures has primary interest in the evaluation of epileptic patients. A seizure is the …
EEG based spatial attention shifts detection using time-frequency features on empirical wavelet transform
G Altan, G Altan, G İnat, G Inat - Akıllı Sistemler ve Uygulamaları …, 2021 - asd.islerya.com
The human nervous system has over 100b nerve cells, of which the majority are located in
the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction …
the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction …