title: A Bayesian framework for analyzing iEEG data from a rat model of epilepsy.
abstract: The early detection of epileptic seizures requires computing relevant statistics from multivariate data and defining a robust decision strategy as a function of these statistics that accurately detects the transition from the normal to the peri-ictal (problematic) state. We model the afflicted brain as a hidden Markov model (HMM) with two hidden clinical states (normal and peri-ictal). The output of the HMM is a statistic computed from multivariate neural measurements. A Bayesian framework is developed to analyze the a posteriori conditional probability of being in peri-ictal state given current and past output measurements. We apply this method to multichannel intracortical EEGs (iEEGs) from the thalamo-cortical ictal pathway in an epilepsy rat model. We first define the output statistic as the max singular value of a connectivity matrix computed on the EEG channels with spectral techniques Then, we estimate the HMM transition probabilities from this statistic and track the a posteriori probability of being in peri-ictal state (the "information state variable"). We show how the information state variable changes as a function of time and we predict a seizure when this variable becomes greater than 0.5. This Bayesian strategy significantly improves over chance level and heuristically-chosen threshold-based predictors.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. ssantan5@jhu.edu
authors.forename: Sabato
authors.initials: S
authors.lastname: Santaniello
authors.forename: David L
authors.initials: DL
authors.lastname: Sherman
authors.forename: Marek A
authors.initials: MA
authors.lastname: Mirski
authors.forename: Nitish V
authors.initials: NV
authors.lastname: Thakor
authors.forename: Sridevi V
authors.initials: SV
authors.lastname: Sarma
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity