Automatic detection of high frequency oscillations based on Fuzzy entropy and Fuzzy neural network

Abstract

For the localization of epileptic seizure, it has a guiding significance to detect high frequency oscillations (HFOs) which are novel biomarkers of the seizure onset zone. In this paper, a new method including Fuzzy entropy (FuzzyEn) and Fuzzy neural network (FNN) for automatic HFOs detection is proposed. FuzzyEn and short time energy which is calculated by mean square method are used as the inputs of the FNN classifier. The T-S interference model is applied to the consequent part of the fuzzy rules. The least squares estimation and error back propagation algorithms are used to adjust the consequent and antecedent parameters, respectively. The output of the FNN classifier is utilized to classify the signal as HFOs or as normal activity. The simulation results indicate that FuzzyEn can extract the HFOs features more effectively compared with approximate entropy. Besides, the sensitivity and specificity of the FNN classifier proposed in this paper are obviously higher than those of the artificial neural network (ANN) classifier which has the same number of hidden layers, neurons and epochs with the FNN classifier. It is clear that the classification performance of the FNN classifier is better compared with the ANN classifier.