Search

Electroencephalogram (EEG) signals can be used to identify the human brain in different disease conditions. Nonetheless, it is difficult to detect the subtle and vital differences in EEG simply by visual inspection because of the non-stationary nature of EEG signals. Specifically, in order to find the epileptogenic focus for medical treatment in the case of a partial epilepsy, an intelligent system that can accurately and automatically detect and discriminate focal and non focal groups of EEG signals is required. This will assist clinicians in locating epileptogenic foci before surgery. In this study we propose a novel method for classification between focal and non focal EEG signals based upon empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third and fourth IMFs components are extracted which contain most of the EEG signals' energy and are considered to be the predominant IMFs. Second, phase space of the two IMFs componets is reconstructed, in which the properties associated with the EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between the focal and non focal groups of EEG signals. Third, neural networks are then used as the classifier with feature vectors as the input to distinguish between focal and non focal EEG signals based on the difference of system dynamics between the two groups. Finally, experiments are carried out on the Bern Barcelona database to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved accuracy on the 50 pairs and 3750 pairs of EEG signals is reported to be 96 % and 95.37 % , respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of focal EEG signals in the clinical application. [ABSTRACT FROM AUTHOR]

• We explore the ability of FAWT without reconstruction analysis in the feature extraction of epilepsy signals. • Fuzzy distribution entropy is firstly introduced to discriminate the focal and non-focal EEG signals, and a satisfied classification performance is achieved using the combination of log energy entropy and fuzzy distribution entropy. • In order to obtain more reliable results, different classifiers have been considered in this work. • The entire Bern Barcelona database is used to verify the proposed method which makes the results more statistically significant. Surgical treatment is one of the most important methods to cure or control drug-resistant epilepsy, and preoperative localization of epileptic lesions plays an important role in the success of a surgery. Given that the manual diagnosis takes time and effort, an automatic detection system is needed to aid clinical diagnosis. Therefore, in the present study, a new automatic focal electroencephalogram (EEG) detection algorithm combining flexible analytic wavelet transform (FAWT) with entropies was put forward. The differential focal (F) and non-focal (NF) EEG signals were decomposed into 15-level sub-bands using FAWT, and this was followed by computing log energy entropy (LEE) and fuzzy distribution entropy (fDistEn) of the detail coefficients of 15 sub-bands and the differential EEG signal. Kruskal–Wallis one-way analysis of variance (ANOVA) was adopted to select the statistically significant features, and five classifiers including general regression neural network (GRNN), support vector machine (SVM), least squares support vector machine (LS-SVM), K-nearest neighbor (KNN), and fuzzy K-Nearest neighbors (fKNN) were then used to verify the effectiveness of the selected features. The proposed methodology was tested on the Bern Barcelona database, and a maximum accuracy of 94.80 % was achieved in distinguishing F and NF EEG signals via LS-SVM classifier. The results suggest that the proposed method is a valuable approach to aid clinicians in locating the epileptic focus in practical application. [ABSTRACT FROM AUTHOR]