Detection of partial seizure: An application of fuzzy rule system for wearable ambulatory systems

Abstract

Electroencephalography (EEG) plays an intelligent role, especially EEG based health diagnosis of brain disorder, as well as brain-computer interface (BCI) applications. One such research field is related to epilepsy. The EEG based methods are not will designed for pre-occurrence recognition scheme to detect and predict partial seizure for epileptic patients. The system even becomes more complicated if the detection system is to be designed for ubiquitous operations, for the identification of people with seizure disabilities. In this case, the patients are not restricted to the clinical environment in which many devices are involved to the patient externally while he/she can continue daily activities. This paper demonstrates a classification method by using Fuzzy Logic System to identify, predict the Partial Seizure from Epileptic data. Here the paper shows preliminary results of the normal state, pre-seizure state and seizure state of the subject's brain signal data. This can be observed and the algorithm with the detection structure can produce cautioning signals for epileptic seizure.