A Bayesian framework for analyzing iEEG data from a rat model of epilepsy.

Sabato Santaniello, David L. Sherman, Marek A Mirski, Nitish V Thakor, Sridevi V. Sarma

Research output: Contribution to journalArticle

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.

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Electroencephalography
Rats
Epilepsy
Stroke
Statistics
Hidden Markov models
Brain
Seizures

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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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.",
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AU - Thakor, Nitish V

AU - Sarma, Sridevi V.

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AB - 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.

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