Epileptic seizure detection with linear and nonlinear features.
Automatic seizure detection is significant in both diagnosis of epilepsy and relieving the heavy workload of inspecting prolonged EEG. This paper presents a new seizure detection method for multi-channel long-term EEG. The fractal intercept derived from fractal geometry is extracted as a novel nonlinear feature of EEG signals, and the relative fluctuation index is calculated as a linear feature. The feature vector, consisting of the two EEG descriptors, is fed into a single-layer neural network for classification. Extreme learning machine (ELM) algorithm is adopted to train the neural network. Finally, post-processing including smoothing, channel fusion, and collar technique is employed to obtain more accurate and stable results. Both the segment-based and event-based assessments are used for the performance evaluation of this method on the 21-patient Freiburg dataset. The segment-based sensitivity of 91.72% and specificity of 94.89% were achieved. For the event-based assessment, this method yielded a sensitivity of 93.85% with a false detection rate of 0.35/h.
School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan, China.
Qi
Q
Yuan
Weidong
W
Zhou
Yinxia
Y
Liu
Jiwen
J
Wang
D000465:Algorithms; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted