Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Classification of Alzheimer's disease patients using metrics of oculo-motors

, , ,

DOI: http://dx.doi.org/10.15439/2021F32

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 403–407 ()

Full text

Abstract. Ocular information was observed during a set of dementia tests involving  participants with two diagnostic levels of illness, such as having  Alzheimer's Disease (AD), a mild level of cognitive impairment (MCI)  patients, and a control group. The number of participants was 26.  Features of changes in pupil size and in the central position of both  eyes were compared between three levels.  There are significant differences in some of the metrics between the  levels in the earlier test sessions. The possibility of classification  was confirmed using the extracted features, and the contributions of  some features were examined.

References

  1. K. Aoki, T. T. Ngo, I. Mitsugami, F. Okura, M. Niwa, Y. Makihara, Y. Yagi, and H. Kazui, “Early detection of lower MMSE scores in elderly based on dual-task gait,” IEEE Access, vol. 7, pp. 40 085–40 094, 2019.
  2. J. Beatty, “Task-evoked pupillary response, processing load, and the structure of processing resources,” Psychological Bulletin, vol. 91, no. 2, pp. 276–292, 1982.
  3. J. Kuhlmann and M. Böttcher, Eds., Pupillography: Principles, Methods and Applications. Munchen, Germany: W. Zuckschwerdt Verlag, 1999.
  4. M. Nakayama and M. Katsukura, “Development of a system usability assessment procedure using oculo-motors for input operation,” Universal Access in Information Society, vol. 10, no. 1, pp. 51–68, 2011.
  5. M. Nakayama, W. Nowak, H. Ishikawa, K. Asakawa, and Y. Ichibe, “Discovering irregular pupil light responses to chromatic stimuli using waveform shapes of pupillograms,” EURASIP J. in Bioinformatics and System Biology, vol. #18, pp. 1–14, 2014.
  6. W. Nowak, M. Nakayama, T. Kręcicki, E. Trypka, A. Andrzejak, and A. Hachoł, “Analysis for extracted features of pupil light reflex to chromatic stimuli in Alzheimer’s patients,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 5, pp. 1–10, November 2019, e4.
  7. W. Nowak, M. Nakayama, T. Kręcicki, and A. Hachoł, “Detection procedures for patients of Alzheimer’s disease using waveform features of pupil light reflex in response to chromatic stimuli,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 6, pp. 1–11, December 2020, e6.
  8. M. F. Folstein, S. E. Folstein, and P. R. McHugh, “MINI-MENTAL STATE – a practical method for grading the cognitive state of patients for the clinician,” Journal of Psychiatric Research, vol. 12, pp. 189–198, 1975.
  9. Z. S. Nasreddine, N. A. Phillips, V. Bédirian, S. Charbonneau, V. Whitehead, I. Collin, J. L. Cummings, and H. Chertkow, “The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment,” Journal of American Geriatrics Society, vol. 53, pp. 695–699, 2005.
  10. V. Peysakhovich, M. Causse, S. Scannella, and F. Dehais, “Frequency analysis of a task-evoked pupillary response: Luminance-independent measure of mental effort,” International Journal of Psychophysiology, vol. 97, pp. 30–37, 2015.
  11. K. Ukai, “Pupil,” in SHIKAKU JYOUHOU SYORI HANDOBUKKU (Handbook of Visual Information Processing), Japan Society of Vision Science, Ed. Tokyo, Japan: Asakura Shoten, 2001.
  12. Y. Ebisawa and M. Sugiura, “Influences of target and fixation point conditions on characteristics of visually guided voluntary saccade,” The Journal of the Institute of Image Information and Television Engineers, vol. 52, no. 11, pp. 1730–1737, 1998.