Integrating manual preprocessing with automated feature extraction for improved rodent seizure classification.

Journal: Epilepsy & behavior : E&B
PMID: 39983590

Abstract

HYPOTHESIS/OBJECTIVE: Rodent models of epilepsy can help with the search for more effective drug candidates or neuromodulatory therapies. Yet, preclinical screening of candidate options for anti-epileptic drugs (AED) using rodent models may require hours of video monitoring. Data processing is also time-consuming, subjective, and error-prone. This study aims to develop an AI-enabled quantitative analysis of rodent behavior, including epilepsy stage classification.

Authors

  • An Yu
    Department of Computer Science, University at Albany, Albany NY, United States.
  • Mannut Singh
    Department of Neuroscience & Experimental Therapeutics, Albany Medical College, Albany NY, United States.
  • Abhineet Pandey
    Department of Computer Science, University at Albany, Albany NY, United States.
  • Elizabeth Dybas
    Department of Neuroscience & Experimental Therapeutics, Albany Medical College, Albany NY, United States.
  • Aditya Agarwal
    Department of Neuroscience & Experimental Therapeutics, Albany Medical College, Albany NY, United States.
  • Yifan Kao
    Department of Neuroscience & Experimental Therapeutics, Albany Medical College, Albany NY, United States.
  • Guangliang Zhao
    GE Aerospace, Niskayuna NY, United States.
  • Tzu-Jen Kao
    GE HealthCare Technology and Innovation Center, Niskayuna NY, United States.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Damian S Shin
    Department of Neuroscience & Experimental Therapeutics, Albany Medical College, Albany NY, United States.
  • Ming-Ching Chang
    Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, 115201, Taiwan. mchang2@albany.edu.

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