Pattern and Rule Mining for Identifying Signatures of Epileptic Patients from Clinical EEG Data

Abstract

Epilepsy is a neurological condition of human being, mostly treated based on the patients’ seizure symptoms, often recorded over multiple visits to a health-care facility. The lengthy time-consuming process of obtaining multiple recordings creates an obstacle in detecting epileptic patients in real time. An epileptic signature validated over EEG data of multiple similar kinds of epilepsy cases will haste the decision-making process of clinicians. In this paper, we have identified EEG data derived signatures for differentiating epileptic patients from normal individuals. Here we define the signatures with the help of various machine learning techniques, viz., feature selection and classification, pattern mining, and fuzzy rule mining. These signatures will add confidence to the decision-making process for detecting epileptic patients. Moreover, we define separate signatures by incorporating few demographic features like gender and age. Such signatures may aid the clinicians with the generalized epileptic signature in case of complex decisions.