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Post-Traumatic Stress Disorder Severity Prediction on Web-based Trauma Recovery Treatments through Electrodermal Activity Measurements

机译：基于网络的创伤恢复治疗通过皮肤电活动测量的创伤后应激障碍严重程度预测。

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摘要

Recent studies have shown evidences regarding trauma recovery through web-based interventions. Currently, a widespread protocol is to assess trauma severity by answering the PTSD Checklist (PCL) questionnaire, which requires subjects' intervention. This thesis explores the feasibility of automatically predicting changes in trauma severity, deltaPCL, through the analysis of electrodermal activity measurements in order not to bother subjects after the intense mental effort experienced during the trauma recovery treatment. Furthermore, the automatic trauma severity prediction can provide web-based trauma recovery treatments with tools to monitor subjects' progress during treatment, so its contents can be adapted to the subjects' needs.;This analysis is performed on the EASE dataset, and evaluates the performance of a trauma severity predictor system implemented when predicting global or symptom cluster-wise deltaPCL scores. The machine learning models presented in this work are assessed using 3 different feature sets extracted from skin conductance signals. One of these feature sets is proposed in this thesis, while the other ones are already existing and open-source. The baseline for all evaluations is the system performance using CSE-T scores as input, since CSE has proven to be a strong indicator of changes in trauma severity symptoms in various psychological studies.;According to the results obtained, the MSEs mean measured when predicting global deltaPCL scores with a system that uses C=1 and gamma = 10--2 equals 122.870 and 122.488 when inputting CSE-T scores and TEAP set of features extracted from skin conductance signals to the system, respectively. Furthermore, the p-value = 0.9772 obtained between both performances indicates that it seems feasible to replace CSE-T information with skin conductance signs to predict deltaPCL scores. On the other hand, the MSEs mean measured with a system that employs C=100 and gamma = 10--1 equals 294.916 and 138.277 when employing CSE-T scores and TEAP set of features as system input, respectively. Moreover, the p-value = 0.0046 obtained between both performances indicates that the use of skin conductance signals significantly outperforms the baseline. Additionally, similar results to those presented are obtained in both scenarios when predicting symptom cluster-wise deltaPCL scores.
机译：最近的研究显示了有关通过基于网络的干预来恢复创伤的证据。当前，一种广泛的方案是通过回答PTSD检查表（PCL）问卷来评估创伤的严重程度，这需要受试者的干预。本论文探讨了通过对皮肤电活动测量的分析来自动预测创伤严重程度deltaPCL的变化的可行性，以免在创伤恢复治疗过程中经历强烈的脑力劳动后再打扰受试者。此外，自动创伤严重程度预测可以为基于Web的创伤恢复治疗提供工具，以监测受试者在治疗过程中的进展，因此其内容可以适应受试者的需求。;此分析在EASE数据集上进行，并评估预测总体或症状聚类deltaPCL分数时实施的创伤严重程度预测器系统的性能。使用从皮肤电导信号中提取的3种不同特征集评估了这项工作中提出的机器学习模型。本文提出了其中一个功能集，而其他功能集已经存在并且是开源的。所有评估的基准都是使用CSE-T得分作为输入的系统性能，因为在各种心理学研究中CSE被证明是创伤严重程度症状变化的有力指标。根据获得的结果，MSE的平均值是在预测时当将CSE-T分数和从皮肤电导信号提取的TEAP特征集输入到系统时，使用C = 1和gamma = 10--2的系统的全局deltaPCL分数分别等于122.870和122.488。此外，两个性能之间获得的p值= 0.9772表示用皮肤电导符号代替CSE-T信息以预测deltaPCL分数似乎是可行的。另一方面，当使用CSE-T分数和TEAP特征集作为系统输入时，使用C = 100和gamma = 10--1的系统测得的MSE平均值分别为294.916和138.277。此外，两次测试之间获得的p值= 0.0046表示使用皮肤电导信号明显优于基线。此外，当预测症状聚类增量PCL分数时，在两种情况下均获得与所呈现结果相似的结果。

著录项

  • 作者

    Mallol-Ragolta, Adria.;

  • 作者单位

    University of Colorado Colorado Springs.;

  • 授予单位 University of Colorado Colorado Springs.;
  • 学科 Electrical engineering.;Computer science.;Mental health.
  • 学位 M.S.E.E.
  • 年度 2018
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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