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Abstract
Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.
Highlights
Epilepsy, a chronic neurological disorder in which patients suffer from recurring seizures, affects 1–3% of the world population [1]
We investigate and compare the performance of the baseline cepstral vector with that of the two composite vectors (first comprising 9 cepstral coefficients and 9 velocity cepstral coefficients, while the second comprising 9 cepstral coefficients, 9 velocity cepstral coefficients, and 9 acceleration cepstral coefficients) to discriminate the general EEG database provided by Andrzejak et al [16] into normal, seizure-free, and seizure classes using probabilistic neural networks (PNN) and accounting for the challenge of unbalanced data sets
To arrive at near-optimal window length, first we study the impact of window length, W, on the overall accuracy on the abovementioned seven different classification problems
Summary
A chronic neurological disorder in which patients suffer from recurring seizures, affects 1–3% of the world population [1] It is characterized by the occurrence of recurrent unprovoked epileptic seizures, which are episodic, rapidly evolving, and temporary events. 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 very short. The epileptic seizure detection methods, usually, aim to detect patterns in EEG recordings
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