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.

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

“…BNDB includes seizure, nonseizure, and normal EEG signals, and we utilized this database for the validation of our method. Other studies that have validated their algorithms using BNDB are summarized in Table . Sharma et al proposed the time‐frequency flexible wavelet transform and fractal dimension, and the performance of their method was only approximately 0.03% higher than that of our method in the classification between ZO and S classes.…”

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

“…Zhou et al (2013) have applied Lacunarity feature with Bayesian linear discriminant analysis (BLDA) classifier for epileptic seizure detection and showed a classification rate of 96.25 %. Samiee et al (2015) introduced rational discrete short time Fourier transforms (DSTFT) with multilayer perception (MLP) neural network for classification of epileptic seizures from normal EEGs. Bajaj and Pachori (2013) have applied the Hilbert transformation of intrinsic mode functions to detect the focal temporal lobe epilepsy.…”

“…Designing an automated system to detect epilepsy on time would be helpful and time-saving for the neurologist. Lately, numerous mechanized frameworks have been proposed for the forecast and recognition of epilepsy with distinctive time and frequency domain features, non-linear features with pattern classifiers (Gotman and Deng 1991;Acharya et al 2012a, b, c;Bajaj and Pachori 2013;Wang et al 2013;Venkataraman et al 2014;Samiee et al 2015;Faust et al 2015;Du et al 2016).…”

Long-term epileptic EEG classification via 2D mapping and textural features

“…In Samiee, Kovacs, and Gabbouj (2015), a new time-frequency representation was proposed based on rational functions which can outperform discrete short-time Fourier transform in analysis of epileptic EEG time-series. Wavelet transform (WT) has been widely utilized for decomposing EEG signals into different frequency sub-bands (Adeli, Zhou, & Dadmehr, 2003;Zandi, Javidan, Dumont, & Tafreshi, 2010).…”