Abstract
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods are usually trained and tested on the same patient due to the interindividual variability. However, the challenging problem of the domain shift between different subjects remains unsolved, resulting in low prevalence of clinical application. In this study, a generic model based on the domain adaptation (DA) technique is proposed to alleviate such problems. Ensemble learning is employed by developing a hierarchical vote collective of seven DA modules over multi-modality data, such that the predictive performance is improved by training multiple models. Moreover, to increase the feasibility of its implementation, this study mimics the data distribution of clinical sampling and tests the model under this simulated realistic condition. Based on the performance of seven subnetworks, the applicability of each DA algorithm for seizure prediction is evaluated, which is the first study that provides the assessment. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can reduce the domain gap effectively compared with previous studies.
Highlights
1.1 Epilepsy BackgroundEpilepsy is a cerebral anomaly with the transient occurrence of unexpected seizures caused by excessive or hypersynchronous neuronal activities [1]
The training and validation sets consist of existing patient data and one seizure of the target subject, while the remaining target seizures served as the testing set
The combined data are partitioned into five folds, and 80% of the samples are assigned to the training set, while the remaining 20% is named for the validation set to prevent overfitting
Summary
Epilepsy is a cerebral anomaly with the transient occurrence of unexpected seizures caused by excessive or hypersynchronous neuronal activities [1]. It is the second most clinically significant neurological disorder, which affects approximately 1.0% of the world’s population [2]. The reliable seizure prediction device, which refers to anticipating an upcoming seizure based on continuous electroencephalogram (EEG) signals, is an emerging and important demand for drug-resistant individuals accounting for about 30% of the epileptic [3, 4]. EEG is a commonly used type of physiological signal that measures the epileptic brain activity, which records rhythmic information induced by coordinated neuronal firing with characteristic periodicity. Many EEG-based algorithms adopting the datadriven technique have been presented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
