In this paper, a classification method is presented using an Fuzzy Inference Engine to detect the incidences of pre-seizures in real/raw Epilepsy data. The system distinguishes between 'Normal', 'Pre-Seizure' and 'Seizure' states using on-the-fly calculated features representing the statistical measures for specifically filtered signals from the raw data. It was noticed that for a large number of cases, the seizure waveforms manifest higher energy components during the seizure episodes as compared to the normal brain activity in specific bands of frequencies. Same is also true for a separate band of frequency where the energy levels change from higher to lower when a patient goes from Normal to a Seizure state. This fact has been exploited in this paper and specific filter has been developed to isolate the seizure band. The Fuzzy inference system (FIS) has been developed on the calculated measures for the filtered signal from this band and classification is performed on the basis of certain experimental thresholds. The complexity of calculations has been kept quite low which makes the algorithm highly suitable for implementation in a small micro-controller environment with near-real-time operation. This gives a more practical functionality for such a system to be used in a wearable fashion over the existing Electroencephalogram (EEG) based seizure detection systems due to their complex pattern classification methodologies. Based on the presented technique, a wearable ubiquitous system can be easily developed with applications in personal healthcare and clinical usage. In this case, the users are not necessarily restricted to the clinical environment in which many devices are connected to the patient externally. The wearable devices allow the user to continue daily activities while being monitored for seizure incidents. This will provide them with a window of 30 seconds before a seizure would occur. Although, a small amount of time, but can be very useful for the patient to change his/her position in order to avoid additional harm that could be inflicted on them while they are seizing. For example, a person is driving or handling power tools can stop, a person carrying a baby can lie-down, etc. © 2014 The Science and Information (SAI) Organization.