A computer-based statistical pattern recognition for Doppler spectral waveforms of intracranial blood flow.
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.
Miao
J
J
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30332, USA.
Benkeser
P J
PJ
Nichols
F T
FT
D001185:Artificial Intelligence; D001783:Blood Flow Velocity / Q000502:physiology; D001810:Blood Volume / Q000502:physiology; D001921:Brain / Q000098:blood supply*; D002536:Cerebral Arteries / Q000000981:diagnostic imaging; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation; D004292:Dominance, Cerebral / Q000502:physiology; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D007427:Intracranial Pressure / Q000502:physiology; D008432:Mathematical Computing; D008838:Microcomputers; D017585:Ultrasonography, Doppler, Transcranial / Q000295:instrumentation*; D014655:Vascular Resistance / Q000502:physiology