Minimum classification error using time-frequency analysis

C. Breakenridge, M. Mesbah

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

1 Citation (Scopus)

Abstract

For certain classes of signals, such as time varying signals, classical classification algorithms are not suitable. Hence, time-frequency based techniques are employed for classification of these types of signals. In this paper we propose data-driven time frequency representations kernel optimization, that leads to the minimum classification error (MCE) for nonstationary signal classification. Our central issue is to determine the optimal kernel parameters and best distance measure to achieve the MCE performance measure. The minimum classification error achievable using optimized kernels is investigated for two types of nonstationary signals; namely simulated chirp signals and real-life newborn EEG signals. For the EEG signals a classification error as low as 4.6% was achieved.

Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages717-720
Number of pages4
ISBN (Electronic)0780382927, 9780780382923
DOIs
Publication statusPublished - 2003
Event3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003 - Darmstadt, Germany
Duration: Dec 14 2003Dec 17 2003

Other

Other3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
CountryGermany
CityDarmstadt
Period12/14/0312/17/03

Keywords

  • Algorithm design and analysis
  • Chirp
  • Electroencephalography
  • Kernel
  • Optimization methods
  • Pediatrics
  • Signal analysis
  • Signal processing algorithms
  • Testing
  • Time frequency analysis

ASJC Scopus subject areas

  • Signal Processing

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