Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals
Background Epilepsy is one of the most commonly seen neurologic disorders worldwide
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
Hybrid attention network for epileptic EEG classification
Y Zhao, J He, F Zhu, T Xiao, Y Zhang… - … Journal of Neural …, 2023 - World Scientific
Automatic seizure detection from electroencephalography (EEG) based on deep learning
has been significantly improved. However, existing works have not adequately excavate the …
has been significantly improved. However, existing works have not adequately excavate the …
A deformable CNN architecture for predicting clinical acceptability of ECG signal
The degraded quality of the electrocardiogram (ECG) signals is the main source of false
alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to …
alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to …
Electroencephalogram signal classification based on Fourier transform and Pattern Recognition Network for epilepsy diagnosis
Q Gao, AH Omran, Y Baghersad, O Mohammadi… - … Applications of Artificial …, 2023 - Elsevier
Epilepsy is a central nervous system (CNS) disorder that affects nerve cells in the brain and
produces seizures in which consciousness is lost. People with epilepsy have frequent …
produces seizures in which consciousness is lost. People with epilepsy have frequent …
An intelligent optimized deep learning model to achieve early prediction of epileptic seizures
Seizure prediction from electroencephalogram (EEG) time series data and a sequential
deep learning (DL) predictor substantially boosts epileptic patients' quality of life. However, a …
deep learning (DL) predictor substantially boosts epileptic patients' quality of life. However, a …
[HTML][HTML] Classification of health deterioration by geometric invariants
Abstract Background and Objectives Prediction of patient deterioration is essential in
medical care, and its automation may reduce the risk of patient death. The precise …
medical care, and its automation may reduce the risk of patient death. The precise …
Effective epileptic seizure detection by classifying focal and non-focal EEG signals using human learning optimization-based hidden Markov model
Electroencephalogram (EEG) recordings are analyzed to make a diagnosis of neurological
diseases like epilepsy before surgical intervention. It is essential to examine the EEG signals …
diseases like epilepsy before surgical intervention. It is essential to examine the EEG signals …
Enhancing EEG signal analysis with geometry invariants for multichannel fusion
Automated computer-aided diagnosis (CAD) has become an essential approach in the early
detection of health issues. One of the significant benefits of this approach is high accuracy …
detection of health issues. One of the significant benefits of this approach is high accuracy …
[HTML][HTML] A channel-wise attention-based representation learning method for epileptic seizure detection and type classification
Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is
a crucial patient-dependent step for the treatment selection process. The selection of the …
a crucial patient-dependent step for the treatment selection process. The selection of the …
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: a systematic review
Machine learning algorithms for seizure detection have shown great diagnostic potential,
with recent reported accuracies reaching 100%. However, few published algorithms have …
with recent reported accuracies reaching 100%. However, few published algorithms have …