Electroencephalography-based feature extraction using complex network for automated epileptic seizure detection

Electroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k-nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10-fold cross-validation criterion. In performance evaluation of proposed method with healthy, seizure-free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy-processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3-class problems mainly for detection of seizure-free interval and seizure signals and acceptable results for 2-class and 5-class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.