Epilepsy is a chronic disorder of the central nervous system that occurs irregularly and unpredictably, due to the temporary electrical disturbances in the brain. According to World Health Organization (WHO), approximately 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally [1]. It predisposes individuals to experience recurrent seizures. Electroencephalogram (EEG) is a technique used to measure the electrical activity of the brain signals for the diagnosis of neurological disorders, and it also paves the way for seizure detection using scalp and intra-cranial EEGs as the input data. In this paper, we have proposed a method for non-linear feature based epileptic seizure detection by extracting five features namely Entropy, Mean, Skewness, Standard Deviation and Band Power. The classification techniques used are K-nearest neighbor (KNN) and Support vector machine (SVM) which gave an accuracy of 95.33% and 100% respectively.
Non-linear Analysis of Intracranial Electroencephalogram Recordings with Approximate Entropy and Lempel-Ziv Complexity for Epileptic Seizure Detection
