Nonlinear dynamics measures for automated EEG-based sleep stage detection
Background: The brain's continuous neural activity during sleep can be monitored by
electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five …
electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five …
Recent advances in health monitoring of civil structures
H Qarib, H Adeli - Scientia Iranica, 2014 - scientiairanica.sharif.edu
This paper presents a review of recent advances made in vibration-based Structural Health
Monitoring (SHM), using the responses of the structure to an excitation. The review is …
Monitoring (SHM), using the responses of the structure to an excitation. The review is …
Epileptic seizures prediction using machine learning methods
Epileptic seizures occur due to disorder in brain functionality which can affect patient's
health. Prediction of epileptic seizures before the beginning of the onset is quite useful for …
health. Prediction of epileptic seizures before the beginning of the onset is quite useful for …
Automatic seizure detection using fully convolutional nested LSTM
The automatic seizure detection system can effectively help doctors to monitor and diagnose
epilepsy thus reducing their workload. Many outstanding studies have given good results in …
epilepsy thus reducing their workload. Many outstanding studies have given good results in …
Computer-aided diagnosis of depression using EEG signals
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very
tedious to interpret visually and highly difficult to extract the significant features from them …
tedious to interpret visually and highly difficult to extract the significant features from them …
A robust methodology for classification of epileptic seizures in EEG signals
Drug inefficiency in patients with refractory seizures renders epilepsy a life-threatening and
challenging brain disorder and stresses the need for accurate seizure detection and …
challenging brain disorder and stresses the need for accurate seizure detection and …
[HTML][HTML] Epileptic seizure detection based on imbalanced classification and wavelet packet transform
Q Yuan, W Zhou, L Zhang, F Zhang, F Xu, Y Leng… - seizure, 2017 - Elsevier
Purpose Automatic seizure detection is significant for the diagnosis of epilepsy and the
reduction of massive workload for reviewing continuous EEG recordings. Methods …
reduction of massive workload for reviewing continuous EEG recordings. Methods …
Epileptic seizure detection with EEG textural features and imbalanced classification based on EasyEnsemble learning
Imbalance data classification is a challenging task in automatic seizure detection from
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …
Denoising sparse autoencoder-based ictal EEG classification
Y Qiu, W Zhou, N Yu, P Du - IEEE Transactions on Neural …, 2018 - ieeexplore.ieee.org
Automatic seizure detection technology can automatically mark the EEG by using the
epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic …
epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic …
Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification
In this paper, the efficiently extracted and reduced features using deep long short-term
memory (DLSTM) of the epileptic EEG signal integrated with minimum variance kernel …
memory (DLSTM) of the epileptic EEG signal integrated with minimum variance kernel …