Epileptogenic localization is a critical factor for successful epilepsy surgery. Determining the epileptogenic hippocampus with single channel intracranial electroencephalography (iEEG) recording is beneficial to decrease the risk of infection compared with that based on multi-channel iEEGs. Delay permutation entropy (DPE) methodology is presented in this study to measure iEEG with different delay lag based on focal epileptogenic zone. A total of 1600 20-s epileptic iEEG are evaluated and are used as features to classify epileptogenic and non-epileptogenic zone. Experimental results show that the DPE index of epileptogenic iEEG is significant lower than that of non-epileptogenic hemisphere when delay lag ranges from 5 to 30 (p=0.01). In addition, the accuracy of identifying epileptogenic region with the DPE index is increased when the delay lag between 5 and 25, compared to the performance of the PE index.
This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, 1, 3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 . In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.Electronic supplementary materialThe online version of this article (doi:10.1007/s40708-014-0003-x) contains supplementary material, which is available to authorized users.
Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.
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