1887
Volume 2014, Issue 1
  • E-ISSN: 2223-506X

Abstract

About 1–3% of the world population suffers from epilepsy. A long term inpatient/ambulatory electroencephalogram (EEG), lasting from a few hours to several days, which definitely contains hallmarks of epilepsy, is required clinically to diagnose, monitor and localize the epileptogenic zone. The traditional method relies on well-trained neurophysiologists who visually inspect the entire lengthy EEG signals, which is tedious and time-consuming. Therefore, many automated epileptic detection systems have been developed and such systems significantly reduce the time taken to review off-line the long-term EEG recordings and facilitate the neurologist to diagnose and treat more patients in a given time. There are not many studies that have explored, to a sufficient depth, the features used in other domains of signal processing, for example, the Teager energy cepstrum (TECEP), attempted use in seizure detection. Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the EEG recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through static and dynamic features derived from TECEP. We compared the performance of the baseline TECEP and its two composite vectors with those of the traditional cepstrum (CEP) and its corresponding two composite vectors, in EEG epileptic seizure detection. The first composite vector includes velocity cepstral coefficients and the second includes velocity, as well as, acceleration cepstral coefficients. The comparison is tried on eight different classification problems which encompass all the possible discriminations in the medical field related to epilepsy, using pattern recognition neural network (PRNN). In this case, it is found that the overall performance of both the Teager energy composite vectors excels those of traditional cepstral composite vectors. The static and dynamic features derived from TECEP outperform those derived from CEP, suggesting their suitability in epilepsy seizure detection.

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2014-03-01
2019-08-20
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