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 unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis
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 …
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 …
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 …
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 …
A deep fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
Epileptic seizure detection with an end-to-end temporal convolutional network and bidirectional long short-term memory model
Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …