Bivariate Autoregressive State-Space Modeling of Psychophysiological Time Series Data

Authors

Daniel M. Smith, University of Rhode Island
Mohammadreza Abtahi, University of Rhode Island
Amir Mohammad Amiri, University of Rhode Island
Kunal Mankodiya, University of Rhode IslandFollow

Document Type

Conference Proceeding

Date of Original Version

2016

Abstract

Heart rate (HR) and electrodermal activity (EDA) are often used as physiological measures of psychological arousal in various neuropsychology experiments. In this exploratory study, we analyze HR and EDA data collected from four participants, each with a history of suicidal tendencies, during a cognitive task known as the Paced Auditory Serial Addition Test (PASAT). A central aim of this investigation is to guide future research by assessing heterogeneity in the population of individuals with suicidal tendencies. Using a state-space modeling approach to time series analysis, we evaluate the effect of an exogenous input, i.e., the stimulus presentation rate which was increased systematically during the experimental task. Participants differed in several parameters characterizing the way in which psychological arousal was experienced during the task. Increasing the stimulus presentation rate was associated with an increase in EDA in participants 2 and 4. The effect on HR was positive for participant 2 and negative for participants 3 and 4. We discuss future directions in light of the heterogeneity in the population indicated by these findings.

Citation/Publisher Attribution

Smith, D. M., Abtahi, M., Amiri, A. M., & Mankodiya, K. (2016, August 16-20). Bivariate Autoregressive State-Space Modeling of Psychophysiological Time Series Data. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL, USA. doi: 10.1109/EMBC.2016.7591932

Available at: http://dx.doi.org/10.1109/EMBC.2016.7591932

Comment

Daniel M. Smith is from the Department of Psychology. Mohammadreza Abtahi, Amir Mohammad Amiri, and Kunal Mankodiya are from the Department of Electrical, Computer and Biomedical Engineering.