Detecting PTSD Using Neural and Physiological Signals: Recommendations from a Pilot Study

Manasa Kalanadhabhatta, Shaily Roy, Trevor Grant, Asif Salekin, Tauhidur Rahman, Dessa Bergen-Cico

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Post-traumatic stress disorder (PTSD) is a serious condition that is characterized by negative mood and affect, hyperarousal, irritability, and reactivity, as well as deterioration of cognitive processes such as attention and memory. Timely identification and treatment of PTSD symptoms can significantly improve symptom management and recovery. However, accurate prediction of PTSD outside clinical settings is often challenging. In this work, we investigate whether deficits in cognitive performance can be used to classify individuals with and without PTSD. We further examine whether neural and physiological signals such as prefrontal cortex activity, heart rate, respiration, and electrodermal activity recorded in conjunction with cognitive task performance can be leveraged to improve PTSD classification. Our results indicate that working memory tasks can achieve an F1 score of 0.80 at classifying individuals with PTSD, which can be further improved to 0.91 by combining multimodal information from neurophysiological signals. Based on our findings, we provide recommendations for in-the-wild PTSD classification.

Original languageEnglish (US)
Title of host publication2023 11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350327434
DOIs
StatePublished - 2023
Event11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023 - Cambridge, United States
Duration: Sep 10 2023Sep 13 2023

Publication series

Name2023 11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023

Conference

Conference11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023
Country/TerritoryUnited States
CityCambridge
Period9/10/239/13/23

Keywords

  • PTSD
  • cognitive performance
  • neural activity
  • physiological signals
  • post-traumatic stress disorder
  • wearables

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Media Technology
  • Cognitive Neuroscience

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