Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform

2014/09/24 by Kaveh Samiee, Peter Kovacs, Péter Kovács +1 · 413 citations
Computer Science · Mathematics · Neuroscience · Psychology · #Artificial intelligence #Blind Source Separation Techniques #Classifier (UML) #Computer science #Computer vision #EEG and Brain-Computer Interfaces #Electroencephalography #Epileptic seizure #Feature extraction #Fourier transform #Mathematics #Neuroscience #Pattern recognition (psychology) #Psychology #Speech recognition #Time Series Analysis and Forecasting #Time–frequency analysis

paper · doi:10.1109/tbme.2014.2360101

published in IEEE Transactions on Biomedical Engineering 62(2), 541-552 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2014/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

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.

Citations

• PhysioBank, PhysioToolkit, and PhysioNet

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