A phase-locked loop epilepsy network emulator

详细信息    查看全文

• 作者：P.D. Watson^a ; ^b ; ^{pwatson1@illinois.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; K.M. Horecka^a ; ^bAuthor Vitae ; R. Ratnam^a ; ^d ; ^eAuthor Vitae ; N.J. Cohen^a ; ^b ; ^cAuthor Vitae
• 关键词：Epilepsy emulation ; Neural network ; Approximate entropy ; Electrocorticography ; Phase locked loop
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：1245-1249
• 全文大小：726 K

文摘

Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al., 2011 [6]) and a physiological entropy measure (approximate entropy or ApEn, Pincus 1995 [21]), we demonstrate that the entropy of the emulator phases increases dramatically during ictal periods across all ECoG recording sites and across all animals in the sample. Further, this increase precedes the observable voltage spikes that characterize seizure activity in the ECoG data. These results suggest that the ENE is sensitive to phase-domain information in the neural circuits measured by ECoG and that an increase in the entropy of this measure coincides with increasing likelihood of seizure activity. Understanding this unpredictable phase-domain electrical activity present in ECoG recordings may provide a target for seizure detection and feedback control.