Add to ORKGabstract: Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors
Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as Elec...
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relatio...
Current epileptic seizure prediction algorithms are generally based on the knowledge of seizure oc...
Epilepsy affects approximately 1% of the population and can be as benign as causing social awkwardne...
Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable...
This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm ...
A seizure prediction method is proposed by extracting global features using phase correlation betwee...
This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm ...
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This...
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This...
Combining multiple linear univariate features in one feature space and classifying the feature space...
In this paper, a robust seizure detection system using scalp EEG signal is presented. Two most impor...
Epilepsy, characterized by recurrent, unpredictable seizures, is one of the most common neuropsychia...
Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, ...
Epilepsy is a common neurological disorder that affects over 90 million people globally — 30-40% of ...
Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as Elec...
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relatio...
Current epileptic seizure prediction algorithms are generally based on the knowledge of seizure oc...
Epilepsy affects approximately 1% of the population and can be as benign as causing social awkwardne...
Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable...
This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm ...
A seizure prediction method is proposed by extracting global features using phase correlation betwee...
This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm ...
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This...
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This...
Combining multiple linear univariate features in one feature space and classifying the feature space...
In this paper, a robust seizure detection system using scalp EEG signal is presented. Two most impor...
Epilepsy, characterized by recurrent, unpredictable seizures, is one of the most common neuropsychia...
Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, ...
Epilepsy is a common neurological disorder that affects over 90 million people globally — 30-40% of ...
Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as Elec...
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relatio...
Current epileptic seizure prediction algorithms are generally based on the knowledge of seizure oc...