A computer-based statistical pattern recognition for Doppler spectral waveforms of intracranial blood flow

Jianwei Miao, Paul J. Benkeser, Fenwick T Nichols

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

A computer-based statistical pattern recognition system has been developed for the analysis of transcranial Doppler (TCD) spectral waveforms of the intracranial middle cerebral artery with varying degrees of increased intracranial pressure. This system extracts multidimensional features from TCD waveforms and performs a cluster analysis of those features. The system can automatically recognize the pattern of spectral waveform and classify it as a normal, abnormal, or borderline subclass of TCD spectral waveform. An optimum decision function was generated based on the Bayes Gaussian classifier. The accuracy of the Bayes Gaussian model the spectral waveforms reaches 100% by estimating posterior probability and using the resubstituting method of estimating misclassification in the training TCD data.

Original languageEnglish (US)
Pages (from-to)53-63
Number of pages11
JournalComputers in Biology and Medicine
Volume26
Issue number1
DOIs
StatePublished - Jan 1 1996

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Keywords

  • Bayes classifier
  • Blood flow
  • Canonical discriminant technique
  • Cluster analysis
  • Doppler waveforms
  • K means algorithm
  • Pattern recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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