Abstract
In this paper, we explore how a number of novel methods for visualizing and analyzing differences in eye-tracking data, including scanpath length, Levenshtein distance, and visual transition frequency, can help to elucidate the methods clinicians use for interpreting 12-lead electrocardiograms (ECGs). Visualizing the differences between multiple participants' scanpaths simultaneously allowed us to answer questions including: do clinicians fixate randomly on the ECG, or do they apply a systematic approach?; is there a relationship between interpretation accuracy and visual behavior? Results indicate that practitioners have very different visual search strategies. Clinicians who incorrectly interpret the image have greater scanpath variability than those who correctly interpret it, indicating that differences between practitioners in terms of accuracy are reflected in different eye-movement behaviors. The variation across practitioners is likely to be the result of differential training, clinical role and expertise.
Highlights
Scanpath analysis -- examination of the sequence in which people fixate on different parts of a stimulus -- is widely used in eye-tracking research (Groner, Walder, & Groner, 1984; Holmqvist et al, 2011)
Scanpaths can be considered in terms of the sequence of Areas of Interest” (AOIs) (Areas Of Interest defined by the researcher) that a participant visits, which can be compared with string metrics such as the Levenshtein distance, or in terms of the spatial positions/alignment of fixations
Using simultaneous scanpath visualization to investigate the relationship between accuracy and eye movement during medical image interpretation
Summary
Scanpath analysis -- examination of the sequence in which people fixate on different parts of a stimulus -- is widely used in eye-tracking research (Groner, Walder, & Groner, 1984; Holmqvist et al, 2011). We apply our methods to quantitatively analyze clinicians’ visual behavior in the medical sub-domain of electrocardiology This field lends itself to scanpath analysis, as electrocardiogram (ECG) data consists of signals from 12 sources, which are presented in different equal-sized areas on a single output. The study presented in this paper represents a subsection of wider exploratory work related to the visual behavior of humans interpreting ECGs using eye-tracking technology. Understanding this process could provide essential information for improving automated interpretation software. The scanpath can be formed from a set of locations represented by the order in which the AOI is visited (in computing terms, a string) One such method for the calculation of differences between two string sequences is the Levenshtein distance. In this work we apply visualization methods to explore similarities and differences between participants’ scanpaths as they carry out an ECG interpretation task
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