[HTML][HTML] Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Electroencephalography (EEG) is an important tool for studying the human brain activity and
epileptic processes in particular. EEG signals provide important information about …
epileptic processes in particular. EEG signals provide important information about …
[HTML][HTML] Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy
Nearly one-third of patients with epilepsy continue to have seizures despite optimal
medication management. Systems employed to detect seizures may have the potential to …
medication management. Systems employed to detect seizures may have the potential to …
Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
Epileptic seizure is the most common disorder of human brain, which is generally detected
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
Classification of seizure and nonseizure EEG signals using empirical mode decomposition
In this paper, we present a new method for classification of electroencephalogram (EEG)
signals using empirical mode decomposition (EMD) method. The intrinsic mode functions …
signals using empirical mode decomposition (EMD) method. The intrinsic mode functions …
A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG
In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has
been widely used in computer-aided signal analysis of epileptic electroencephalography …
been widely used in computer-aided signal analysis of epileptic electroencephalography …
Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy
In this study, a new scheme was presented for detecting epileptic seizures from electro-
encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new …
encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new …
Feature extraction and recognition of ictal EEG using EMD and SVM
Automatic seizure detection is significant for long-term monitoring of epilepsy, as well as for
diagnostics and rehabilitation, and can decrease the duration of work required when …
diagnostics and rehabilitation, and can decrease the duration of work required when …
Classification of EEG signals using neural network and logistic regression
Epileptic seizures are manifestations of epilepsy. Careful analyses of the
electroencephalograph (EEG) records can provide valuable insight and improved …
electroencephalograph (EEG) records can provide valuable insight and improved …
LMD based features for the automatic seizure detection of EEG signals using SVM
T Zhang, W Chen - IEEE Transactions on Neural Systems and …, 2016 - ieeexplore.ieee.org
Achieving the goal of detecting seizure activity automatically using electroencephalogram
(EEG) signals is of great importance and significance for the treatment of epileptic seizures …
(EEG) signals is of great importance and significance for the treatment of epileptic seizures …
Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions
Epilepsy is a neurological disorder which is characterized by transient and unexpected
electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used …
electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used …