Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Focal and non-focal epilepsy localization: A review
The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which
has affected million people in the world. Hence, an early detection of the focal epileptic …
has affected million people in the world. Hence, an early detection of the focal epileptic …
Epileptic EEG classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
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 …
A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals
Background EEG signals obtained from Mild Cognitive Impairment (MCI) and the
Alzheimer's disease (AD) patients are visually indistinguishable. New method A new …
Alzheimer's disease (AD) patients are visually indistinguishable. New method A new …
Time–frequency signal processing: Today and future
Most real-life signals exhibit non-stationary characteristics. Processing of such signals
separately in the time-domain or in the frequency-domain does not provide sufficient …
separately in the time-domain or in the frequency-domain does not provide sufficient …
A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis
Z Chen, G Lu, Z **e, W Shang - IEEE Access, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) contains important physiological information that can reflect
the activity of human brain, making it useful for epileptic seizure detection and epilepsy …
the activity of human brain, making it useful for epileptic seizure detection and epilepsy …
Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks
Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges
(IEDs) as distinctive biomarkers of epilepsy has various limitations, including time …
(IEDs) as distinctive biomarkers of epilepsy has various limitations, including time …
Compact convolutional neural network with multi-headed attention mechanism for seizure prediction
X Ding, W Nie, X Liu, X Wang, Q Yuan - International Journal of …, 2023 - World Scientific
Epilepsy is a neurological disorder related to frequent seizures. Automatic seizure prediction
is crucial for the prevention and treatment of epilepsy. In this paper, we propose a novel …
is crucial for the prevention and treatment of epilepsy. In this paper, we propose a novel …
A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy
Epilepsy is one of the grave neurological ailments affecting approximately 70 million people
globally. Detection of epileptic attack is commonly carried out by viewing and analysing long …
globally. Detection of epileptic attack is commonly carried out by viewing and analysing long …