Abstract
Electroencephalograph (EEG) plays a significant role in the diagnostics process of epilepsy, but the detection rate is unsatisfactory when the length of interictal EEG signals is relatively short. Although the deliberate attacking theories for undirected brain network based on node removal method can extract potential network features, the node removal method fails to sufficiently consider the directionality of brain electrical activities. To solve the problems above, this study proposes a feature tensor-based epileptic detection method of directed brain networks. First, a directed functional brain network is constructed by calculating the transfer entropy of EEG signals between different electrodes. Second, the edge removal method is used to imitate the disruptions of brain connectivity, which may be related to the disorder of brain diseases, to obtain a sequence of residual networks. After that, topological features of these residual networks are extracted based on graph theory for constructing a five-way feature tensor. To exploit the inherent interactions among multiple modes of the feature tensor, this study uses the Tucker decomposition method to get a core tensor which is finally reshaped into a vector and input into the support vectors machine (SVM) classifier. Experiment results suggest that the proposed method has better epileptic screening performance for short-term interictal EEG data.
Highlights
Epilepsy is typically diagnosed by epileptic discharges combined with clinical manifestations of patients (Noachtar and Rémi, 2009)
Krishnan et al (2014) developed a novel classification method of spike detection to achieve an 87% detection rate of epileptic discharge, but EEG data must be collected for several hours, which is too long for general experiments
The methods of receivingedge and sending-edge removal can be used to obtain a series of residual networks for extracting topological features
Summary
Epilepsy is typically diagnosed by epileptic discharges combined with clinical manifestations of patients (Noachtar and Rémi, 2009). Sometimes normal EEG signals account for most of the detection time, which makes it hard to detect epileptic discharges (Pittau et al, 2011; Maganti and Rutecki, 2013). A study stated that the detection rate of epileptic discharge was only 19.2% under the 30-min EEG data when 240 epilepsy patients with conscious resting status were examined (Qin and Dou, 2016). Krishnan et al (2014) developed a novel classification method of spike detection to achieve an 87% detection rate of epileptic discharge, but EEG data must be collected for several hours, which is too long for general experiments
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