Photoplethysmogram (PPG) sensors are increasingly used to collect individual heart rate data during laboratory assessments and psychological experiments. PPG sensors are relatively cheap, easy to use, and non-invasive alternatives to the more common electrodes used to produce electrocardiogram recordings. The downside is that these sensors are more susceptible to signal distortion. Often, the most relevant measures for understanding psychological processes that underlie emotions and behaviors are measures of heart rate variability. As with all measures of variability, outliers (i.e., signal artifacts) can have outsized effects on the final estimates; and, given that these scores represent a primary variable of interest in many research contexts, the successful elimination of artefactual points is critical to the ability to make valid inferences with the data. Prior to the development of IBI VizEdit, there was no single, integrated processing and editing pipeline for PPG data. The present pair of studies offers and initial evaluation of the program’s performance. Study 1 is focused on the efficacy of a novel approach to imputing sections of particularly corrupted PPG signal. Study 2 tests the ability of trained editors to reliably use IBI VizEdit as well as the validity of estimates of cardiac activity during a prescribed set of laboratory tasks. Study 1 suggests that the novel imputation approach, under certain conditions and using certain parameterizations may hold promise as a means of accurately imputing missing sections of data. However, Study 1 also clearly demonstrates the need for further refinement and the consideration of alternative implementations. The results from Study 2 indicate that IBI VizEdit can be reliably used by trained editors and that estimates of cardiac activity derived from its output are likely valid.