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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...
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...
This paper describes a classification method is presented using an empirical Rule-base System to detect the occurrences of Partial Seizures from Epilepsy data, which can be implemented in any embedded system as a wearable detection system. The system distinguishes between ‘Normal’ and ‘Seizure’ state using on-the-fly calculated features representing the statistical measures for specifically filtered...
Numerous theories and models have been developed to associate various findings or in relating EEG patterns to develop a software simulators. In this paper, a Dynamic model for simulating the EEG signal has been developed with empirical reference to real EEG signals from patients suffering from Seizure and Partial Seizure. Real EEG data set can be obtained in either .edf or .tdms or .txt formats from...
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