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A novel EEG-complexity-based feature and its application on the epileptic seizure detection

  • Shu-Ling Zhang
  • Bo ZhangEmail author
  • Yong-Li Su
  • Jiang-Ling Song
Original Article
  • 15 Downloads

Abstract

The neurophysiology system is a complex network of nerves and cells, which carries messages to and from the brain and spinal cord to various parts of the body. Exploring complexity of the system can be contributed to understand diverse neurophysiological abnormalities, which may further result in different kinds of neurological disorders. In this paper, we present a novel analyzing framework to characterize the complexity of neurophysiological system, under which a specific weighted FPE-complexity-based feature (W-FPE-F) is extracted from EEG and then applied into the automated epileptic seizure detection. Combining with extreme learning machine (ELM) and support vector machine (SVM), performances of the proposed method are finally verified on two open EEG databases. Simulation results show that the proposed method does a good job in detecting the epileptic seizure, particularly, it is able to avoid the undesirable detection performance caused by individual divergence effectively.

Keywords

Neurophysiology system Complexity analysis Feature extraction Feature weighting Automated seizure detection Electroencephalography (EEG) 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61473223 and the Natural Science Foundation of Shaanxi Province, China under Grant 2017JM1043.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information EngineeringXijing UniversityXi’anChina
  2. 2.School of mathematicsNorthwest UniversityXi’anChina
  3. 3.National and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information ServiceNorthwest UniversityXi’anChina

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