a Department of Psychology , The University of Texas at Austin , Austin , TX , USA.
J Sex Marital Ther. 2018;44(6):566-590. doi: 10.1080/0092623X.2018.1436627. Epub 2018 Mar 6.
Vaginal photoplethysmography is the most commonly used method of assessing women's genital sexual arousal. Raw photoplethysmograph data consist of a series of peaks and troughs, and movement by the participant results in artifacts indicated by unusually high or low peak-to-trough amplitudes. The gold-standard approach to artifact detection involves visual inspection by a trained experimenter and manual removal of artifacts from the data set, however, this process is time-consuming and subject to human error. We present an automated data-processing procedure that uses a series of smoothing regression splines to model the data and identify outliers. The automated procedure was applied to a set of neutral data and sexual-arousal response data, and artifacts identified were compared to artifacts identified by the standard approach of visual inspection. The automated method showed acceptable accuracy in terms of sensitivity and specificity comparable to the manual-processing method. The automated procedure could reduce human error and data-processing time for studies using vaginal photoplethysmography.
阴道光体积描记法是评估女性生殖器性唤起最常用的方法。原始光体积描记数据由一系列的峰和谷组成，参与者的运动导致异常高或低的峰谷幅度的伪影。人工制品检测的金标准方法涉及由受过训练的实验员进行视觉检查和手动从数据集删除人工制品，然而，这个过程很耗时并且容易出现人为错误。我们提出了一种自动数据处理程序，该程序使用一系列平滑回归样条来对数据进行建模并识别异常值。该自动程序应用于一组中性数据和性唤起反应数据，并且识别出的人工制品与视觉检查的标准方法识别出的人工制品进行了比较。自动方法在灵敏度和特异性方面具有可接受的准确性，与手动处理方法相当。该自动程序可以减少使用阴道光体积描记法的研究中的人为错误和数据处理时间。
