A low complexity seizure prediction algorithm

Michael J. Brown, Theoden Netoff, Keshab K. Parhi

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

A new low complexity seizure prediction algorithm is proposed. The algorithm achieves high sensitivity and low false positive rates in 10 out of 18 epileptic patients from the Freiburg database. Its primary achievement is two orders of magnitude computational complexity reduction. The reduced complexity makes an implantable medical device application realizable. In the subset of ten highly predictable patients average sensitivity is 96%, average specificity is 0.25 false positives per hour, and 13.5% of time is spent in false alarms. For all eighteen patients tested, the average sensitivity is 83%, the average specificity is 0.38 false positives per hour, and the amount of time spent in false alarms is 21.1%. This result may be compared with sensitivity of 97.5%, specificity of 0.27 false positives per hour, and 13% of time is spent in false alarms of prior results without complexity reduction.

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Keywords

  • Epilepsy
  • Feature Selection
  • Implantable device
  • Seizure Prediction
  • Support Vector Machine (SVM)

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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