中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (52): 9794-9797.doi: 10.3969/j.issn.1673-8225.2010. 52.026

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

脑干听觉诱发电位波形的自动检测及新标识

高瑞静，孙  迎，李越囡   

  1. 上海理工大学医疗器械与食品学院，上海市  200093
  • 出版日期:2010-12-24 发布日期:2010-12-24
  • 作者简介:高瑞静★，女，1986年生，河南省安阳市人，汉族，上海理工大学生物医学工程专业在读硕士，主要从事医学信息学方面的研究。 yunjing0204@163.com
  • 基金资助:

    上海市教育委员会自然科学基金资助项目(04EB25)。

Automated detection and identification method for auditory brainstem response waveforms

Gao Rui-jing, Sun Ying, Li Yue-nan   

  1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai  200093, China
  • Online:2010-12-24 Published:2010-12-24
  • About author:Gao Rui-jing★, Studying for master’s degree, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China yunjing0204@163.com
  • Supported by:

     the Natural Science Foundation of Shanghai Education Commission, No. 04EB25

PDF

437

被引次数

11

摘要：

背景：过去，研究者们提出了很多脑干听觉诱发电位自动检测的方法，但是这些方法主要适用于听力级阈值的估计，并且只能够检测到有限个波峰。
目的：通过脑干听觉诱发电位波形特点和数理方法的研究，提出了一种新的波峰标识算法——余弦求和算法，该算法可以较容易准确地检测到脑干听觉诱发电位感觉级和听力级的所有波峰。
方法：在上海新华医院对3名无听力障碍的成年人进行脑干听觉诱发电位检测，实验采用短声刺激，刺激频率为23 Hz，各种声强下，在10 ms时间内采集1 024个数据点，得到了大量的先验实验数据。采用设计好的波峰标识新算法，对每组数据进行波峰自动标识的仿真实验,检验算法的准确性和实用性。
结果与结论：把脑干听觉诱发电位子波波峰潜伏期的临床经验值作为自动标识的参照，大量的仿真实验表明，新算法能够很准确地标识出脑干听觉诱发电位的各个子波波峰点，可以快速地计算出子波潜伏期值和波幅值。

关键词: 脑干听觉诱发电位, 波峰自动标识, 波峰潜伏期, 余弦求和算法, 数字化医学

Abstract:

BACKGROUND: Researchers proposed a lot of auditory brainstem response (ABR) automatic detection methods. But these methods were mainly applied to estimate hearing threshold level, and can only detect a finite number of peaks.
OBJECTIVE: By studying the ABR waveform characteristics and related mathematical methods, to propose a new peak identification algorithm---cosine summation algorithm, which can more accurately detect all the sensation level and hearing level peaks of ABR.
METHODS: ABR detection experiments were performed on 3 Individuals without hearing impairment in Xinhua Hospital. With short sound stimulation, by the stimulus frequency of 23 Hz, at various sound stimulus intensity, 1 024 data points were collected within 10 ms, and a priori experimental data was obtained. With the designed new peak identification algorithm, simulation experiments on each data were carried out, and the accuracy and practicality of the algorithm was tested.
RESULTS AND CONCLUSION: The clinical experience values of ABR wavelet peak latency were regarded as the reference values for automatic identification. A large number of experiments showed that the new algorithm can accurately identify each ABR wavelet peak and quickly calculate the values of sub-wave latency and the amplitude.

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