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A study of EEG feature complexity in epileptic seizure prediction [r-libre/2844]

Jemal, Imene; Mitiche, Amar, & Mezghani, Neila (2021). A study of EEG feature complexity in epileptic seizure prediction. Applied Sciences, 11 (4), 1579. https://doi.org/10.3390/app11041579

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Content : Published Version
License : Creative Commons Attribution.
Item Type: Journal Articles
Refereed: Yes
Status: Published
Abstract: The purpose of this study is (1) to provide EEG feature complexity analysis in seizure prediction by inter-ictal and pre-ital data classification and, (2) to assess the between-subject variability of the considered features. In the past several decades, there has been a sustained interest in predicting epilepsy seizure using EEG data. Most methods classify features extracted from EEG, which they assume are characteristic of the presence of an epilepsy episode, for instance, by distinguishing a pre-ictal interval of data (which is in a given window just before the onset of a seizure) from inter-ictal (which is in preceding windows following the seizure). To evaluate the difficulty of this classification problem independently of the classification model, we investigate the complexity of an exhaustive list of 88 features using various complexity metrics, i.e., the Fisher discriminant ratio, the volume of overlap, and the individual feature efficiency. Complexity measurements on real and synthetic data testbeds reveal that that seizure prediction by pre-ictal/inter-ictal feature distinction is a problem of significant complexity. It shows that several features are clearly useful, without decidedly identifying an optimal set.
Official URL: https://www.mdpi.com/2076-3417/11/4/1579
Depositor: Ayena, Johannes
Owner / Manager: Johannes Ayena
Deposited: 16 Jan 2023 19:35
Last Modified: 16 Jan 2023 19:35

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