Enhancing performance of convolutional neural network-based epileptic electroencephalogram diagnosis by asymmetric stochastic resonance
Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is
clinically diagnosed primarily by electroencephalography. Several intelligent systems have …
clinically diagnosed primarily by electroencephalography. Several intelligent systems have …
An elastic self-adjusting technique for rare-class synthetic oversampling based on cluster distortion minimization in data stream
HK Fatlawi, A Kiss - Sensors, 2023 - mdpi.com
Adaptive machine learning has increasing importance due to its ability to classify a data
stream and handle the changes in the data distribution. Various resources, such as …
stream and handle the changes in the data distribution. Various resources, such as …
EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction
H Li, J Liao, H Wang, AZ Chang'an, F Yang - Computers in Biology and …, 2024 - Elsevier
Background: The seizure prediction algorithms have demonstrated their potential in
mitigating epilepsy risks by detecting the pre-ictal state using ongoing …
mitigating epilepsy risks by detecting the pre-ictal state using ongoing …
Graphical Insight: Revolutionizing Seizure Detection with EEG Representation
Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in
the brain. These seizures manifest as various symptoms including muscle contractions and …
the brain. These seizures manifest as various symptoms including muscle contractions and …