一种基于计算机的颅内血流多普勒频谱波形统计模式识别方法。

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

作者信息

Miao J, Benkeser P J, Nichols F T

机构信息

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30332, USA.

出版信息

Comput Biol Med. 1996 Jan;26(1):53-63. doi: 10.1016/0010-4825(95)00029-1.

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

PMID:8654053

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.

摘要

已经开发出一种基于计算机的统计模式识别系统，用于分析颅内压不同程度升高时颅内大脑中动脉的经颅多普勒（TCD）频谱波形。该系统从TCD波形中提取多维特征，并对这些特征进行聚类分析。该系统可以自动识别频谱波形的模式，并将其分类为TCD频谱波形的正常、异常或临界子类。基于贝叶斯高斯分类器生成了一个最优决策函数。通过估计后验概率并使用训练TCD数据中估计错误分类的重新代入方法，贝叶斯高斯模型对频谱波形的准确率达到了100%。