中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (43): 8086-8089.doi: 10.3969/j.issn.1673-8225.2010.43.027

• 数字化骨科 digital orthopedics • 上一篇    下一篇

一种自动标记异常血压和血流速度信号的算法

储  霞，吴效明，黄岳山   

  1. 华南理工大学生物医学工程系，广东省广州市 510006
  • 出版日期:2010-10-22 发布日期:2010-10-22
  • 作者简介:储霞★，女，1985年生，湖北省随州市人，汉族，华南理工大学在读硕士，主要从事生物医学信号处理研究。 chuxia-420@163.com
  • 基金资助:

    广东省科技计划项目(2009B030801004)资助。

An automatic marking method for detecting abnormal signals in arterial blood pressure and cerebral blood flow velocity waveforms

Chu Xia, Wu Xiao-ming, Huang Yue-shan   

  1. Department of Biomedical Engineering, South China University of Technology, Guangzhou  510006, Guangdong Province, China
  • Online:2010-10-22 Published:2010-10-22
  • About author:Chu Xia★, Studying for master’s degree, Department of Biomedical Engineering, South China University of Technology, Guangzhou 510006, Guangdong Province, China chuxia-420@163.com
  • Supported by:

    the Science and Technology Development Program of Guangdong Province, No. 2009B030801004*

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摘要：

背景：目前，异常血压信号检测的主要方法是提取每个心动周期血压信号的特征，再分类识别。由于大多数血压信号是正常的，只有少数异常，如果对每个周期的信号进行检测，效率是比较低的。因此关于异常血流速度信号检测的文章不多见。
目的：基于异常信号的频域和时域特征，提出了一种自动标记异常血压和血流速度信号的算法。
方法：先从频域上筛选出可能的异常信号，再分析这些可能异常信号的时域特征，从而最终标记出所有的异常信号。
结果与结论：异常信号的标记和识别是预处理比较关键的一步，这步对后期信号分析的正确性起决定性作用。目前对于信号的异常标记大都是基于形态学分析判断，往往要遍历所有数据。先从信号的频域特征分析，删选需要判别的对象，提高了计算速度。该算法能够识别出大多数的异常信号，当然也存在一部分的错误标记信号，因为该算法的判断标准还要考虑到后期的处理需要(是否影响信号均值)，所以对有些信号人为判断是较模糊的。下一步工作是希望建立异常信号的严格判断标准，并在算法的时域特征选择和判断上做进一步完善。

关键词: 异常信号, 血压, 脑血流速度, 信号算法, 数字化医学　

Abstract:

BACKGROUND: Current abnormal blood pressure waveform detection algorithms are developed by definition of each beat and extraction of feature at beat-level resolution. These methods are time-consuming for normal signals are much more than abnormal signals. There are few reports about detection abnormal blood flow waveforms.
OBJECTIVE: To propose a classification algorithm for detecting faulty signals in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) which are normally caused by the calibration of the recording devices and artifacts during the data collection.
METHODS: The feature extraction process combined frequency domain and time-domain methods, which allowed a simple 2-class thresholding technique to differentiate the faulty signals from the recorded data.
RESULTS AND CONCLUSION: Marking and recognition of abnormal signals is key for pretreatment, which is decisive for accurate signal analysis. Current detection of abnormal signals is based on morphological analysis, involving all data. Calculation speed of this algorithm is fast because the analysis was based on both distinctions in frequency and time domain and not just in time-domain. For the lack of a standard of ‘abnormal’ ABP and CBFV signals and many uncertain factors in human mark, this algorithm offer a possibility of unifying the standard and avoiding the subjective influence of human. This algorithm detected the majority of abnormal signals, but some errors exist, because the identification criteria of this algorithm influence later processing. Therefore, subjective identification of some signals is unclear. Future study should establish rigorous identification criteria of abnormal signals and improve time-domain distinctions selection and identification.

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