Zhen Zhang1, Ziyi Chen2, Yi Zhou3, Shouhong Du4, Yang Zhang5, Tian Mei1, Xianghua Tian6. 1. Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China. 2. Department of Neurology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China. Electronic address: chenziyi@mail.sysu.edu.cn. 3. Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China; College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China. Electronic address: zhouyi@mail.sysu.edu.cn. 4. College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China. Electronic address: dudinggood@163.com. 5. Department of Neurology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China. 6. College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China.
Abstract
OBJECTIVE: In this study on the analysis of EEG signals for seizure prediction, we used a combination of statistically relevant theory and nonlinear dynamics to maximize the sensitivity of nonlinear analysis and improved prediction accuracy (PA) and effectiveness. METHODS: First, a physiological reference range of approximate entropy (ApEn) was set up based on normal EEG data. Second, using the concept of global optimization, all EEG electrodes were used in the study regardless of the location of epileptic foci, and the five-electrode group with the strongest synchronization discharge was employed as the optimal electrode group for the next prediction. We set a warning signal when the ApEn values of the data were below the reference range in five electrodes at the same time. RESULTS: From the overall 142.7-h EEG signal containing 37 seizures from nine epileptics, our PA was 94.59%, the false prediction rate was 0.084/h, and the mean prediction time was 26.64min. CONCLUSION: Combining statistically relevant theory and nonlinear dynamics can significantly improve the sensitivity of the nonlinear analysis in seizure prediction. SIGNIFICANCE: This method may provide a theoretical foundation for the development of a clinical real-time warning system for patients with partial epilepsy.
OBJECTIVE: In this study on the analysis of EEG signals for seizure prediction, we used a combination of statistically relevant theory and nonlinear dynamics to maximize the sensitivity of nonlinear analysis and improved prediction accuracy (PA) and effectiveness. METHODS: First, a physiological reference range of approximate entropy (ApEn) was set up based on normal EEG data. Second, using the concept of global optimization, all EEG electrodes were used in the study regardless of the location of epileptic foci, and the five-electrode group with the strongest synchronization discharge was employed as the optimal electrode group for the next prediction. We set a warning signal when the ApEn values of the data were below the reference range in five electrodes at the same time. RESULTS: From the overall 142.7-h EEG signal containing 37 seizures from nine epileptics, our PA was 94.59%, the false prediction rate was 0.084/h, and the mean prediction time was 26.64min. CONCLUSION: Combining statistically relevant theory and nonlinear dynamics can significantly improve the sensitivity of the nonlinear analysis in seizure prediction. SIGNIFICANCE: This method may provide a theoretical foundation for the development of a clinical real-time warning system for patients with partial epilepsy.
Authors: Jason S Naftulin; Omar J Ahmed; Giovanni Piantoni; Jean-Baptiste Eichenlaub; Louis-Emmanuel Martinet; Mark A Kramer; Sydney S Cash Journal: Epilepsia Date: 2018-06-13 Impact factor: 5.864