Automatic detection of naturally occurring epilepsy in dogs using intracranial electroencephalogram signals

Abstract

Abstract Automatic detection of epilepsy has become a crucial tool for the treatment of medical-refractory epilepsy and it has gained a great attention of researchers during recent years. Two major bottlenecks in the development of automated epilepsy detection systems are dearth of precise data and deficiency of general algorithms which detects epilepsy with minimum computational requirements. Thanks to advances in electroencephalogram (EEG) machines, scalp or intracranial EEG machines which provides high quality signals are now available. Robust algorithms which detect epilepsy with less computational power and memory is the prevalent requirement. In this work, an automatic detection method is presented to detect naturally occurring epilepsy in dogs by analyzing intracranial EEG (iEEG). A Modified log energy entropy feature is extracted from original iEEG signals and its first and second derivatives. One way analysis of variance (ANOVA) test is also carried out to study the efficiency of extracted features. The performance of the proposed method is evaluated using UPenn - Mayo Clinic Seizure Detection Challenge dataset. The result of k-fold cross validation in k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are higher than that of the state-of-the-art methods.