[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
Epileptic seizure detection using machine learning: Taxonomy, opportunities, and challenges
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …
[HTML][HTML] Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …
world's population. Seizure detection and classification are difficult tasks and are ongoing …
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 …
Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture
Heavy-duty CNC machines are important equipment in manufacturing large-scale and high-
end products. During the machining processes, a significant amount of heat is generated to …
end products. During the machining processes, a significant amount of heat is generated to …
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
Electrical bio-signals have the potential to be used in different applications due to their
hidden nature and their ability to facilitate liveness detection. This paper investigates the …
hidden nature and their ability to facilitate liveness detection. This paper investigates the …
Intelligent epileptic seizure detection and classification model using optimal deep canonical sparse autoencoder
Simple Summary Epileptic seizures are a chronic and persistent neurological illness that
mainly affects the human brain. Since the traditional way of diagnosing epileptic seizures is …
mainly affects the human brain. Since the traditional way of diagnosing epileptic seizures is …
AI-based epileptic seizure detection and prediction in internet of healthcare things: a systematic review
Epilepsy is a neurological condition affecting around 50 million individuals worldwide,
reported by the World Health Organization. This is identified as a hypersensitive disease by …
reported by the World Health Organization. This is identified as a hypersensitive disease by …
Scale-adaptive super-feature based MetricUNet for brain tumor segmentation
Accurate segmentation of brain tumors is very essential for brain tumor diagnosis and
treatment plans. In general, brain tumor includes WT (whole tumor), TC (tumor core) and ET …
treatment plans. In general, brain tumor includes WT (whole tumor), TC (tumor core) and ET …
Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA
Abstract Direct Torque Control (DTC) of Induction Machine (IM) has received increasing
attention due to its high performance and low dependence on machine parameters …
attention due to its high performance and low dependence on machine parameters …