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[勃起功能障碍中的肌电图及计算机辅助解读]

[Electromyograms in erectile dysfunction and computer-assisted interpretation].

作者信息

Gorek M, Hartung C, Stief C G

机构信息

Urologische Klinik, Medizinische Hochschule Hannover.

出版信息

Biomed Tech (Berl). 1997 Mar;42(3):48-54.

PMID:9181828
Abstract

The electromyogram of the corpora cavernosa (CC-EMG) provides information on the autonomic innervation and/or smooth musculature. The interpretation of the CC-EMG, usually by evaluating signal patterns of higher activity, is time-consuming. To improve this situation, a computer-aided diagnosis employing a Microsoft Windows user interface was developed. The computer-aided interpretation is based on digital measurement data obtained using a 170.6 Hz sampling frequency and quantization of 10 V/12 bits of the signal. The first task of the program is to extract signal patterns of higher activity from stored data. To describe these patterns in mathematical terms, the features "relative time position", "relative reproducibility", "portion of normal phases", and "portion of whip phases" are calculated. Characteristic signal forms (normal and whip phases) are identified by means of syntactic pattern recognition. Using fuzzy logic, the features are used to effect pattern evaluation. To summarize the evaluated patterns, the variable "global normality" has been established, and linked to the "global synchronicity" at a second fuzzy logic level, to produce the final diagnosis. Finally, 30 records were evaluated independently by a team of experts and by the computer program. In relation to four established diagnostic classes, a correspondence of 70% was found. Furthermore, the accuracy achieved in each of the individual classes was better than 50%. Discrimination between normal and abnormal evaluation, which is of particular interest, reached 80%.

摘要

阴茎海绵体肌电图(CC - EMG)可提供有关自主神经支配和/或平滑肌组织的信息。通常通过评估较高活性的信号模式来解读CC - EMG,这很耗时。为改善这种情况,开发了一种采用微软视窗用户界面的计算机辅助诊断方法。计算机辅助解读基于使用170.6赫兹采样频率和10伏/12位信号量化所获得的数字测量数据。该程序的首要任务是从存储的数据中提取较高活性的信号模式。为了用数学术语描述这些模式,计算“相对时间位置”“相对再现性”“正常相位比例”和“波动相位比例”等特征。通过句法模式识别来识别特征性信号形式(正常和波动相位)。利用模糊逻辑,这些特征用于进行模式评估。为了总结评估后的模式,建立了变量“全局正常性”,并在第二个模糊逻辑层面将其与“全局同步性”相联系,以得出最终诊断。最后,一个专家团队和计算机程序分别对30份记录进行了独立评估。在四个既定的诊断类别方面,发现一致性为70%。此外,每个单独类别的准确率都超过了50%。特别令人感兴趣的正常与异常评估之间的区分度达到了80%。

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