8 papers with code • 1 benchmarks • 1 datasets
The resulting method yields a computationally and conceptually simple, interpretable model of EEG signals of preictal and interictal states, which shows a good performance for the task of seizure prediction.
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients.
Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it.
We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients.
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly.
In individual patients, 71% had a moderate to large difference (ranksum r > 0. 3) between focal and subclinical seizures in three or more markers.