Detection of post-traumatic stress disorder using learned time-frequency representations from pupillometry

Post-traumatic stress disorder is a major public health concern with a lifetime prevalence rate of 6.1-9.2% in North America. PTSD is known to alter the autonomic nervous system leading to chronic sympathetic arousal including heightened anxiety and hypervigilance. Pupillometry offers a quick and accessible measure of autonomic nervous system imbalances characteristic of PTSD. This study investigates the utility of pupillometry as a biomarker to detect PTSD in a sample of 39 adults with (n = 22) and without (n = 17) PTSD. Participants viewed a 25-minute computer protocol consisting of 5-minute rest phase, 10-minute negative emotionally valent images, and 10-minute guided meditation. We relied on a time-frequency analysis to represent the pupillary responses of two different groups (PTSD-affected individuals and healthy-control subjects). These data were then employed with a CNN network to learn a prediction model. Individuals with PTSD demonstrated increased pupil dilation across the entire protocol. The final outcome revealed an accuracy of 81.09% which indicates the feasibility of using this approach to detecting participants with PTSD in an automated way. Findings from this research have important implications for clinical mental health assessment, diagnostics and treatment.