History of epilepsy detection

Approximately 1% of the people in the world suffer from epilepsy. The electroencephalogram (EEG) signal is used for the purpose of the epileptic detection as it is a condition related to the brain’s electrical activity. Epilepsy is characterized by the occurrence of recurrent seizures in the EEG signal. In majority of the cases, the onset of the seizures cannot be predicted in a short period, a continuous recording of the EEG is required to detect epilepsy. A common form of recording used for this purpose is an ambulatory recording that contains EEG data for a very long duration of even up to one week.

It involves an expert’s efforts in analyzing the entire length of the EEG recordings to detect traces of epilepsy. As the traditional methods of analysis are tedious and time-consuming, many automated epileptic EEG detection systems have been developed in recent years.

With the advent of technology, it is possible to store and process the EEG data digitally. The digital EEG data can be fed to an automated seizure detection system in order to detect the seizures present in the EEG data. Hence, the neurologist can treat more patients in a given time as the time taken to review the EEG data is reduced considerably due to automation. Automated diagnostic systems for epilepsy have been developed using different approaches.

This paper discusses an automated epileptic EEG detection system using two different neural networks, namely, Elman network and probabilistic neural network using a time-domain feature of the EEG signal called approximate entropy that reflects the nonlinear dynamics of the brain activity. ApEn is a recently formulated statistical parameter to quantify the regularity of a time series data of physiological signals.

It was first proposed by Pincus in 1991 and has been predominantly used in the analysis of the heart rate variability and endocrine hormone release pulsatility, estimation of regularity in epileptic seizure time series data, and in the estimation of the depth of anesthesia.

Diambra et al. have shown that the value of the ApEn drops abruptly due to the synchronous discharge of large groups of neurons during an epileptic activity. Hence, it is a good feature to make use of in the automated detection of epilepsy. In this paper, this feature is applied, for the first time, in the automated detection of epilepsy using neural networks.