Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 273–277 (2018)
Abstract. A diagnostic procedure to predict the probability of diagnosing a patient with Alzheimer's Disease (AD) was developed using features of pupil light reflex (PLR) waveforms. 15 features of PLRs for three colours of light pulses at two levels of brightness were measured. Participants were 12 AD patients and 7 control group subjects. A logistic regression analysis was introduced to identify AD patients using two factor scores of features of PLR. The prediction performance of combinations of factor scores for features of PLRs were then evaluated using a test of fitness. An MCMC technique was introduced to estimate the parameters of the regression functions. The model provides a distribution of the probability of diagnosis of AD patients and control group subjects.
- D. F. Fotiou, V. Setergiou, D. Tsiptsios, C. Lithari, M. Nakou, and A. Karlovasitou, “Cholinergic deficiency in Alzheimer’s and Parkinson’s disease: Evaluation with pupillometry,” International Journal of Psychophysiology, vol. 73, pp. 143–149, 2009.
- D. M. Bittner, I. Wieseler, H. Wilhelm, M. W. Riepe, and N. G. Müller, “Repetitive pupil light reflex: Potential marker in Alzheimer’s disease?” Journal of Alzheimer’s Disease, vol. 42, pp. 1469–1477, 2014.
- P. D. Gamlin, D. H. McDougal, and J. Pokorny, “Human and macaque pupil responses driven by melanopisn-containing retinal ganglion cells,” Vision Research, vol. 47, pp. 946–954, 2007.
- A. Kawasaki and R. H. Kardon, “Intrinsically photosensitive retinal ganglion cells,” Journal of Neuro-Ophthalmology, vol. 27, pp. 195–204, 2007.
- 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, no. #18, pp. 1–14, 2014.
- T. Yoshida, K. Ohno-Matsui, S. Ichinose, T. Sato, N. Iwasa, T. C. Saido, T. Hisatomi, M. Mochizuki, and I. Morita, “The potential role of amyloid β in the pathogenesis of age-related macular degeneration,” The Journal of Clinical Investigation, vol. 115, no. 10, pp. 2793–2800, 2005.
- J.-D. Ding, J. Lin, B. Mace, R. Herrmann, P. Sullivin, and C. Rickman, “Targeting age-related macular degeneration with alzheimer’s disease based immunotherapies: Anti-amyloid-β antibody attenuates pathologies in an age-related macular degeneration mouse model,” Vision Research, vol. 48, pp. 339–345, 2008.
- K. Ohno-Matsui, “Parallel findings in age-related macular degeneration and alzheimer’s disease,” Progress in Retinal and Eye Research, vol. 30, pp. 217–238, 2011.
- J. M. Sivak, “The aging eye: Common degenerative mechanisms between the alzheimer’s brain and retinal disease,” Investigative Ophthalmology & Visual Science, vol. 54, no. 1, pp. 871–880, 2013.
- W. Nowak, M. Nakayama, M. Pieniążek, and A. Hachoł, “Feature analyses of pupil light reflex to chromatic stimuli in alzheimer’s patients,” in Proceedings of 2nd International Conference on Frontiers of Signal Processing, 2016, pp. 58–62.
- W. Nowak, A. Ząrowska, E. Szul-Pietrzak, and M. Misiuk-Hojło, “System and measurement method for binocular pupillometry to study pupil size variability,” BioMedical Engineering Online, vol. 13, no. #69, pp. 1–16, 2014.
- Sas/stat 13.1 user’s guide, the mcmc procedure. [Online]. Available: http://support.sas.com/documentation/onlinedoc/stat/131/mcmc.pdf