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 …
A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications
Epileptic seizures are characterised by abnormal neuronal discharge, causing notable
disturbances in electrical activities of the human brain. Traditional methods based on …
disturbances in electrical activities of the human brain. Traditional methods based on …
EEG seizure detection: concepts, techniques, challenges, and future trends
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
EEG-Based Seizure detection using linear graph convolution network with focal loss
Y Zhao, C Dong, G Zhang, Y Wang, X Chen… - Computer methods and …, 2021 - Elsevier
Abstract Background and Objectives: Epilepsy is a clinical phenomenon caused by sudden
abnormal and excessive discharge of brain neurons. It affects around 70 million people all …
abnormal and excessive discharge of brain neurons. It affects around 70 million people all …
A review of recurrent neural network-based methods in computational physiology
Artificial intelligence and machine learning techniques have progressed dramatically and
become powerful tools required to solve complicated tasks, such as computer vision, speech …
become powerful tools required to solve complicated tasks, such as computer vision, speech …
[HTML][HTML] Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis
of several brain disorders, including Alzheimer's disease and epilepsy. Until recently …
of several brain disorders, including Alzheimer's disease and epilepsy. Until recently …
Classify epileptic EEG signals using weighted complex networks based community structure detection
Background Epilepsy is a brain disorder that is mainly diagnosed by neurologists based on
electroencephalogram (EEG) recordings. Epileptic EEG signals are recorded as …
electroencephalogram (EEG) recordings. Epileptic EEG signals are recorded as …
A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation
Electroencephalogram (EEG) signals play an important role in the epilepsy detection. In the
past decades, the automatic detection system of epilepsy has emerged and performed well …
past decades, the automatic detection system of epilepsy has emerged and performed well …
Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG
Epilepsy is a commonly observed long-term neurological disorder that impairs nerve cell
activity in the brain and has a severe impact on people's daily lives. Accurate seizure …
activity in the brain and has a severe impact on people's daily lives. Accurate seizure …