Feature Parameter Optimization for Seizure Detection/Prediction

Abstract

When dealing with seizure detection/prediction problems, there are three main performance metrics that must be optimized: false positive rate, false negative rate, detection delay or, if the problem is seizure prediction, it is desirable to obtain the greatest prediction time achievable. Tuning specific extracted features to individual patients can lead to improved results. The processing window length is also an important parameter whose optimization may significantly affect performance. In this study we propose an approach for selecting the window length for the particular detection/prediction problem. This approach is applicable to other feature parameters suitable for tuning or optimization.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409937

Entities

People

  • B. Litt
  • G. Vachtsevanos
  • J. Echauz
  • M. D. Alessandro
  • R. Esteller

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Data Sets
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Extraction
  • Feature Extraction
  • Machine Learning
  • Military Research
  • Observation
  • Optimization

Readers

  • Computational Modeling and Simulation
  • Operations Research
  • Sensor Fusion and Tracking Systems.