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A novel unsupervised automated epileptic seizure detection methodology based on complex networks synchronous state with EEG signals
Zhang Xia, Xueli Cao, Ren Hao, Wang Min
One of the most challenging tasks with Electroencephalography (EEG) signals is the automated epileptic seizure detection. Traditional automated epileptic seizure detection approaches focus on time or frequency domain to analyse EEG signals. A novel technique for epileptic seizure detection is proposed in this paper, which is based on complex networks synchronous state. The concept of complex network synchronization states has been introduced firstly, and then a network varying with time has been defined by taking the measurement EEG signals as nodes. The dynamic mechanics of the EEG signals network has been quantitatively described by mathematical analysis method. Finally, the mathematical definition, the calculation method and the physical meaning of the EEG signal network synchronous state has been given in this paper. The above theoretical derivation had shown that synchronous state can be employed to assess the level of healthy state with EEG signals. The couple matrix A=(aij)N × N of complex network has been defined by the distance relevance of the measured data, and the left eigenvector (ξ1, ξ2 ,..., ξN), corresponding to the zero feature of the matrix, has been employed to character the local details of complex network nodes. Then, a node fault diagnosis algorithm has been derived based on network synchronous state. The public available EEG database of University of Bonn (UoB), Germany has been used to verify the effectiveness and validity of our proposed method, which has become a benchmark for developing the epilepsy seizure detection systems. Furthermore, the excellent performance of the proposed method has shown that this method can be employed to track the patient healthy state and monitor the moment of epilepsy seizure. This paper has been reported with some references to researchers in related fields.