Comparison of linear, logarithmic and mel-frequency filter-bank energy cepstra in automatic seizure detection using radial basis function neural network

Abstract

Background: Epilepsy is a neurological disorder affecting a very large number of people worldwide. A large fraction of the epilepsy patients have poorly controlled epilepsy. The conventional method relies on experienced neurophysiologists, who visually examine the continuous long-term inpatient/ambulatory electroencephalogram (EEG) signal. This is a tedious and time-consuming, and not a cost effective procedure. Several automated epileptic detection and classification systems have been proposed and such systems facilitate the neurologist to diagnose and treat more patients in a given time. There are not many studies that have explored to an adequate depth the features used in other areas of signal processing, for example, the seldom used feature such as filter-bank energy cepstrum (FBE-CEP), being tried for seizure detection. Methods: Epileptic seizures are abnormal transient recurrent discharges in the brain with signatures manifesting in the EEG recordings by frequency changes and increased amplitudes. We employed static and dynamic features derived from FBE-CEP to capture these changes in amplitude and frequency. We compared the diagnostic performance of the linear, logarithmic, and mel-frequency, baseline FBE-CEP and its two composite vectors in epileptic seizure detection on a standard publicly available EEG database. The comparison was tried on eight different classification problems in the medical field related to epilepsy, using radial basis function neural network. Results: All the three FBE-CEP methods, irrespective of frequency scaling, showed excellent overall performance. Conclusion: The static and dynamic features derived from FBE-CEPs outperform those derived from CEP, suggesting their suitability in epilepsy seizure detection.