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Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform
Abstract
A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.
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An automated system for epilepsy detection using EEG brain signals based on deep learning approach
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Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
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B2-ViT net: broad vision transformer network with broad attention for seizure prediction
Shi, S., & Liu, W. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3346955 · Cited by 124
Metadata
- Authors
- Samiee, K., Kovacs, P., & Gabbouj, M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2015-02
- Volume
- 62
- Issue
- 2
- Pages
- 541-552
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W1981211771
- Cited by
- 411
- References
- 36
- FWCI
- 7.49
- Type
- journal article
- Language
- en
- Open access
- closed
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