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

Jianwei Miao; Paul J. Benkeser; Fenwick T. Nichols

doi:10.1016/0010-4825(95)00029-1

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

Jianwei Miao, Paul J. Benkeser, Fenwick T. Nichols

Neurology

Research output: Contribution to journal › Article › peer-review

5 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 language	English (US)
Pages (from-to)	53-63
Number of pages	11
Journal	Computers in Biology and Medicine
Volume	26
Issue number	1
DOIs	https://doi.org/10.1016/0010-4825(95)00029-1
State	Published - Jan 1996

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

Access to Document

10.1016/0010-4825(95)00029-1

Cite this

@article{a045de87144f496692e029f662d7a79c,

title = "A computer-based statistical pattern recognition for Doppler spectral waveforms of intracranial blood flow",

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.",

keywords = "Bayes classifier, Blood flow, Canonical discriminant technique, Cluster analysis, Doppler waveforms, K means algorithm, Pattern recognition",

author = "Jianwei Miao and Benkeser, {Paul J.} and Nichols, {Fenwick T.}",

year = "1996",

month = jan,

doi = "10.1016/0010-4825(95)00029-1",

language = "English (US)",

volume = "26",

pages = "53--63",

journal = "Computers in Biology and Medicine",

issn = "0010-4825",

publisher = "Elsevier Limited",

number = "1",

}

TY - JOUR

T1 - A computer-based statistical pattern recognition for Doppler spectral waveforms of intracranial blood flow

AU - Miao, Jianwei

AU - Benkeser, Paul J.

AU - Nichols, Fenwick T.

PY - 1996/1

Y1 - 1996/1

N2 - 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.

AB - 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.

KW - Bayes classifier

KW - Blood flow

KW - Canonical discriminant technique

KW - Cluster analysis

KW - Doppler waveforms

KW - K means algorithm

KW - Pattern recognition

UR - http://www.scopus.com/inward/record.url?scp=0030043193&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030043193&partnerID=8YFLogxK

U2 - 10.1016/0010-4825(95)00029-1

DO - 10.1016/0010-4825(95)00029-1

M3 - Article

C2 - 8654053

AN - SCOPUS:0030043193

SN - 0010-4825

VL - 26

SP - 53

EP - 63

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

IS - 1

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this