Authors: Poonam Sheoran; J.S. Saini
Addresses: Department of Biomedical Engineering, D.C.R. University of Science and Technology, Murthal, Sonepat, Haryana, India ' Department of Electrical Engineering, D.C.R. University of Science and Technology, Murthal, Sonepat, Haryana, India
Abstract: The detection and classification of epileptic seizures using the electroencphalogram (EEG) signals has been actively worked upon by the researchers from past few decades. This paper attempts a novel application of time-time transform for analysis of electroencephalogram time-series for epileptic seizure detection by transforming it into secondary time-limited local constituent time-series. This technique (TT-transform) of time-time representation of the EEG time series is derived from S-transform, i.e., Stockwell transform (an extension of the wavelets), a method that represents a non-stationary time series as a set of complex time-localised spectra. With the help of TT-transform, a more informative representation of the time features of EEG signals has been obtained, around a particular point on the time axis which has been seen to prove very effective in seizure detection. As the TT-transform is completely invertible, it indicates frequency filtering and signal to noise improvements in the time domain. Features obtained upon application of TT-transform on EEG time-series are classified using quadratic discriminant analysis and the correct classification rate obtained is 100%.
Keywords: short time Fourier transform; STFT; Stockwell transform; ST; time-time transform; TT; electroencephalogram; EEG; time-frequency analysis; quadratic discriminant analysis; QDA.
International Journal of Biomedical Engineering and Technology, 2019 Vol.30 No.2, pp.113 - 131
Received: 24 Aug 2016
Accepted: 18 Dec 2016
Published online: 27 Jun 2019 *