Discovering Oculometric Patterns to Detect Cognitive Performance Changes in Healthy Youth Football Athletes

Abstract

In this paper, we focus on the application of oculometric patterns extracted from raw eye movements during a mental workload task to assess changes in cognitive performance in healthy youth athletes over the course of a typical sport season. Oculometric features pertaining to fixations and saccades were measured on 116 athletes in pre- and post-season testing. Participants were between 7 and 14 years of age at pre-season testing. Due to varied developmental rates, there were large interindividual performance differences during a mental workload task consisting of reading numbers. Based on different reading speeds, we classified three profiles (slow, moderate, and fast) and established their corresponding baselines for oculometric data. Within each profile, we describe changes in oculomotor function based on changes in cognitive performance during the season. To visualize these changes in multidimensional oculometric data, we also present a multidimensional visualization tool named DiViTo (diagnostic visualization tool). These experimental, computational informatics and visualization methodologies may serve to utilize oculometric information to detect changes in cognitive performance due to mild or severe cognitive impairment such as concussion/mild traumatic brain injury, as well as possibly other disorders such as attention deficit hyperactivity disorders, learning/reading disabilities, impairment of alertness, and neurocognitive function.

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Notes

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    Unlike all other oculometrics, the greater values of oblique and horizontal saccadic velocities indicate better performance. Hence, in DiViTo, polarities of z-scores pertaining to saccadic velocities are reversed to be consistent with other oculometrics.

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Acknowledgments

We acknowledge Dr. Samantha Kleindienst, Dr. David Dodick, Dr. Jennifer Wethe, Dr. Amaal Starling, and the entire Youth Athlete Study Team for facilitating and coordinating the data collection sessions.

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Correspondence to Gaurav N. Pradhan.

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One or more of the investigators associated with this project and Mayo Clinic have a financial interest related to this research.

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Appendices

Appendix 1

Figure 5 shows the evaluation results for exploring different number of clusters from 2 to 10. The graph shows that the lowest Davies-Bouldin value occurs at three clusters, indicating that optimal number of clusters in three for applying K-means clustering on 2-D vector [pre-season reading time, time difference between pre-season and post-season] for 98 healthy youth athletes

Fig. 5
figure5

Results of Davies Bouldin clustering evaluation criterion while exploring different number of clusters from 2 to 10

Appendix 2

Table 4 Comparing oculometrics in-between the three profiles in pre- and post-season.*indicates p < 0.0001

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Pradhan, G.N., Bogle, J.M., Cevette, M.J. et al. Discovering Oculometric Patterns to Detect Cognitive Performance Changes in Healthy Youth Football Athletes. J Healthc Inform Res 3, 371–392 (2019). https://doi.org/10.1007/s41666-019-00045-4

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Keywords

  • Cognitive performance
  • Oculometrics
  • Concussion
  • Eye tracking
  • Multidimensional patterns
  • Pre- and post-season