Optimal control-based bayesian detection of clinical and behavioral state transitions

Sabato Santaniello, David L. Sherman, Nitish V. Thakor, Emad N. Eskandar, Sridevi V. Sarma

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Accurately detecting hidden clinical or behavioral states from sequential measurements is an emerging topic in neuroscience and medicine, which may dramatically impact neural prosthetics, brain-computer interface and drug delivery. For example, early detection of an epileptic seizure from sequential electroencephalographic (EEG) measurements would allow timely administration of anticonvulsant drugs or neurostimulation, thus reducing physical impairment and risks of overtreatment. We develop a Bayesian paradigm for state transition detection that combines optimal control and Markov processes. We define a hidden Markov model of the state evolution and develop a detection policy that minimizes a loss function of both probability of false positives and accuracy (i.e., lag between estimated and actual transition time). Our strategy automatically adapts to each newly acquired measurement based on the state evolution model and the relative loss for false positives and accuracy, thus resulting in a time varying threshold policy. The paradigm was used in two applications: 1) detection of movement onset (behavioral state) from subthalamic single unit recordings in Parkinson's disease patients performing a motor task; 2) early detection of an approaching seizure (clinical state) from multichannel intracranial EEG recordings in rodents treated with pentylenetetrazol chemoconvulsant. Our paradigm performs significantly better than chance and improves over widely used detection algorithms.

Original languageEnglish (US)
Article number6263308
Pages (from-to)708-719
Number of pages12
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number5
StatePublished - 2012
Externally publishedYes


  • Bayesian estimation
  • neural systems
  • optimal control
  • quickest detection (QD)

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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