Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net

Abstract

Epilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography (EEG) performed by trained physicians or technicians but this process is time-consuming and prone to interference, which can negatively impact accuracy. This paper develops a model for epilepsy diagnosis using discrete wavelet transform to analyze sub-bands within the EEG parameter and select EEG characteristics for epilepsy detection. The minimize entropy principle approach is used to build fuzzy membership functions of the characteristics of each brain wave and are then used as the basis for the construction of an associative Petri net model. Using our APN model, the associative Petri net approach provides diagnosis accuracy rates of 93.8%, outperforming similar approaches using decision tree, support vector machine, neural network, Bayes net, naïve Bayes, and tree augmented naïve Bayes. Thus, the proposed approach shows promise for fast, accurate, and objective diagnosis of epilepsy in clinical settings.