Real-Time Seizure State Tracking Using Two Channels: A Mixed-Filter Approach

Mohammad Badri Ahmadi, Alexander Craik, Hamid Fekri Azgomi, Joseph T. Francis, Jose L. Contreras-Vidal, Rose T. Faghih

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Accurate and cost-effective seizure severity tracking is an important step towards limiting the negative effects of seizures in epileptic patients. Electroencephalography (EEG) is employed as a means to track seizures due to its high temporal resolution. In this research, seizure state detection was performed using a mixed-filter approach to reduce the number of channels. We first found two optimized EEG features (one binary, one continuous) using wrapper feature selection. This feature selection process reduces the number of required EEG channels to two, making the process more practical and cost-effective. These continuous and binary observations were used in a state-space framework which allows us to model the continuous hidden seizure severity state. Expectation maximization was employed offline on the training and validation data-sets to estimate unknown parameters. The estimated model parameters were used for real-time seizure state tracking. A classifier was then used to binarize the continuous seizure state. Our results on the experimental data (CHB-MIT EEG database) validate the accuracy of our proposed method and illustrate that the average accuracy, sensitivity, and false positive rate are 85.8%, 91.5%, and 14.3% respectively. This type of seizure state modeling could be used in further implementation of adaptive closed-loop vagus nerve stimulation applications.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2033-2039
Number of pages7
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Other

Other53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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