title: Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model.
abstract: This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Bonan
authors.initials: B
authors.lastname: Shan
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Bin
authors.initials: B
authors.lastname: Deng
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Xile
authors.initials: X
authors.lastname: Wei
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Haitao
authors.initials: H
authors.lastname: Yu
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, People's Republic of China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Li
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D012640:Seizures / Q000175:diagnosis; D013269:Stochastic Processes
chemical_list: D015415:Biomarkers