Identifying Post-Traumatic Stress Symptoms Using Physiological Signals and Data Mining

The number of people diagnosed with anxiety disorders has been increasing in recent years. The correct diagnosis of such disorders is not always a trivial task, sometimes forcing an individual to consult with many clinicians and performing several medical exams. Post-traumatic Stress Disorder (PTSD) is a disorder related to experienced events, which presented a certain degree of threat to an individual. When experiencing situations that refer to past events, an individual may present reactions that trigger physiological changes in the organism such as tachycardia or bradycardia. Many disorders have common symptoms, and identifying these subtleties results in greater diagnosis efficiency and effectiveness. Artificial Intelligence (AI) and Data Mining (DM) techniques have helped specialists in the diagnosis and prevention of diseases and disorders. In our research, we aim at finding new biomarkers to diagnose PTSD analyzing physiological signals with DM techniques. In this paper, we used a dataset from an experiment with civilians that were recently exposed to traumatic events related to violence. Those individuals completed a questionnaire that evaluates the impact of such events through PCL (PTSD Disorder Checklist for DSM-IV) scale. Heart rate and skin conductance signals were collected while viewing emotional and neutral stimuli images. We applied DM techniques and classification algorithms to evaluate and maximize PCL score prediction performance considering those physiological signal data. The best result was obtained with SMO algorithm with its hyperparameters values suggested by an auto-learning procedure, presenting an accuracy of 85.45% (p-value = 0.001), precision of 0.8 (p-value = 0.001), recall of 0.5714 and F-Measure of 0.6667 (p-value = 0.001).