Abstract
About 1–3% of the world population suffers from epilepsy. Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalograph (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through static and dynamic features derived from three Teager energy based filter-bank cepstra (TE-FB-CEPs). We compared the performance of linear, logarithmic, and Mel frequency scale TE-FB-CEPs using radial basis function neural network in general epileptic seizure detection. The comparison is tried on eight different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In a previous study, using traditional cepstrum on the same database, we had found that the composite vectors showed a degraded performance in seizure detection. In this study, however, irrespective of frequency scaling used, it is found that the composite vectors of TE-FB-CEPs maintain excellent overall accuracy in all the eight classification problems.
Highlights
Epilepsy is a chronic neurological disorder with a prevalence of 1–3% of the world population [1]
We handle all the different classification problems proposed by Guo et al [28] and Tzallas et al [29, 30] to encompass all the possible discriminations in the medical field related to epilepsy and compare the performance of our approach with those of other researchers
In the seventh and eighth classification problems (CP nos. 7 and 8), the new classification problems appended by us in this paper, the results are found to be excellent. All these results collectively show a considerable improvement in our approach over many of the previous epilepsy detection methods
Summary
Epilepsy is a chronic neurological disorder with a prevalence of 1–3% of the world population [1]. Many automated epileptic detection systems have been developed using different approaches in the recent years [4]. Such automated systems reduce the time taken to review offline the long-term EEG recordings significantly and facilitate the neurologist to diagnose and treat more patients in a given time. This implies that the selected feature set must be such that besides accuracy in seizure detection, the processing time must be ISRN Biomedical Engineering very short. The wide variety of EEG patterns that characterize the nature of seizures, such as spikes and waves, low-amplitude desynchronization, polyspike activity, rhythmic waves for a wide range of frequencies, and amplitudes, tend to increase the complexity of the automated seizure detection problem
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