Dissertation/Thesis Abstract

Post-Traumatic Stress Disorder Severity Prediction on Web-based Trauma Recovery Treatments through Electrodermal Activity Measurements
by Mallol-Ragolta, Adria, M.S.E.E., University of Colorado Colorado Springs, 2018, 149; 10811113
Abstract (Summary)

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, δPCL, 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 δPCL 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 δPCL scores with a system that uses C=1 and γ = 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 δPCL scores. On the other hand, the MSEs mean measured with a system that employs C=100 and γ = 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 δPCL scores.

Indexing (document details)
Advisor: Boult, Terrance E., Wickert, Mark A.
Commitee: Kalita, Jugal
School: University of Colorado Colorado Springs
Department: College of Engineering and Applied Science–Electrical Engineering
School Location: United States -- Colorado
Source: MAI 57/05M(E), Masters Abstracts International
Subjects: Mental health, Electrical engineering, Computer science
Keywords: CSE-T questionnaire, Machine learning, PCL-5 questionnaire, Signal processing, Skin conductance signals, Trauma severity prediction
Publication Number: 10811113
ISBN: 978-0-355-92259-2
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