[HTML][HTML] A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
[HTML][HTML] Automated epileptic seizure detection in pediatric subjects of CHB-MIT EEG database—a survey
Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures.
Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic …
Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic …
A robust deep learning approach for automatic classification of seizures against non-seizures
Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal
becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on …
becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on …
[Retracted] Enhanced Feature Extraction‐based CNN Approach for Epileptic Seizure Detection from EEG Signals
P Dhar, VK Garg, MA Rahman - Journal of healthcare …, 2022 - Wiley Online Library
One of the most common neurological disorders is epilepsy, which disturbs the nerve cell
activity in the brain, causing seizures. Electroencephalography (EEG) signals are used to …
activity in the brain, causing seizures. Electroencephalography (EEG) signals are used to …
A novel independent rnn approach to classification of seizures against non-seizures
In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by
trained neurologists to provide supports for therapeutic decisions. Manual reviews can be …
trained neurologists to provide supports for therapeutic decisions. Manual reviews can be …
Epileptic signal classification based on synthetic minority oversampling and blending algorithm
D Hu, J Cao, X Lai, J Liu, S Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The scalp electroencephalogram (EEG) has been extensively studied for epileptic signal
classification in the past, but little attention has been paid to the data imbalance among …
classification in the past, but little attention has been paid to the data imbalance among …
Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals
Epilepsy is the second common brain disorder affecting 70 million people worldwide.
Electroencephalogram (EEG) has been widely used for the diagnosis of epileptic seizures …
Electroencephalogram (EEG) has been widely used for the diagnosis of epileptic seizures …
Automated classification of seizures against nonseizures: a deep learning approach
In current clinical practice, electroencephalograms (EEG) are reviewed and analyzed by
well-trained neurologists to provide supports for therapeutic decisions. The way of manual …
well-trained neurologists to provide supports for therapeutic decisions. The way of manual …
Improving outcome prediction for traumatic brain injury from imbalanced datasets using RUSBoosted trees on electroencephalography spectral power
Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML)
that is derived from quantitative electroencephalography (EEG) features has renewed …
that is derived from quantitative electroencephalography (EEG) features has renewed …
[HTML][HTML] Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients
O Nikkinen, T Kolehmainen, T Aaltonen… - Computers in Biology …, 2022 - Elsevier
Background Perioperative acute kidney injury (AKI) is challenging to predict and a common
complication of lower limb arthroplasties. Our aim was to create a machine learning model to …
complication of lower limb arthroplasties. Our aim was to create a machine learning model to …