Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework (Preprint)

Background: Post Traumatic Stress Disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing heart responses to PTSD has shown promise in detecting and understanding the onset of symptoms. Objective: To extract statistical and mathematical approaches that researchers can use to analyze heart rate data in terms of PTSD. Methods: A scoping literature review was conducted to extract heart rate models. Five databases including Medline OVID, Medline EBSCO, CINAHL EBSCO, Embase Ovid, and Google Scholar were searched. Non-English studies, as well as the studies that did not analyze human data, were excluded. 45 articles were chosen to be in the review based on their relevance. Results: We identified four categories of models: descriptive time-independent output, descriptive/time-dependent output, predictive/time-independent output, and predictive/time-dependent output. Descriptive/time-independent output models include Analysis of Variance (ANOVA) and first-order exponential; descriptive time-dependent output includes classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analyzing heart rate-based fluctuation-dissipation theory. Finally, predictive time-dependent output includes time variant method and nonlinear dynamic modeling. Conclusions: All of the identified modeling categories have relevance for PTSD, although modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for inference about PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models, and other factors (e.g., activity level) that have not been analyzed with descriptive models. (JMIR Preprints 10/10/2019:16654) DOI: https://doi.org/10.2196/preprints.16654