Multivariate Data Analysis of Polygraph Charts for Assessment of Biophysical Characteristics
Abstract
In this paper, we propose tools for studying biophysical characteristics of a person by processing polygraph charts. We present the results produced by two methods of multivariate data analysis: the principal component analysis (PCA) and projection on latent structures (PLS). Data of biophysical state of a person during the polygraph testing - galvanic skin response (GSP), photoplethysmogram, blood pressure, and other biophysical characteristics – are processed and analyzed.It is shown that load diagrams are the preferable method to identify the relationship between the biophysical characteristics of a person. The principal component analysis provides an opportunity to assess minor changes in biophysical characteristics of a person and to take into account situational significant identifiers. They can attest to the fact that a possibly significant physical-chemical process runs in a human body.It is demonstrated that the projection on latent structures should be used to develop regression models that can constantly be used in polygraph testing.It is concluded that proposed methods of multivariate data analysis can help in an investigation of biophysical characteristics of a person using polygraph data. Also, the proposed methods are able to detect possible malfunctions in the responsible physical-chemical processes. The obtained results enable solving practical problems of information security matters, for example, the identification of an insider.
DOI 10.14258/izvasu(2018)1-05
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