Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli

Authors

DOI:

https://doi.org/10.4108/eetismla.6070

Keywords:

Pupil Light Reflex, Alzheimer’s Disease, functional data analysis

Abstract

A procedure to detect irregular signal responses to pupil light reflex (PLR) was developed to detect Alzheimer’s Disease (AD) using a functional data analysis (FDA) technique and classification with an Elastic Net. In considering the differences in features of PLRs between AD and normal control (NC) participants, signals of summations and differentials between experimental conditions were analysed. The coefficient vectors for B-spline basis functions were introduced, and the number of basis was controlled for an optimised model. Model trained data was created using a data extension technique in order to enhance the number of participant observations. In the results, the required number of basis functions for differential signals is larger than the number for the their summation signals, and the features of differential signals contribute to classification performance.

Downloads

References

McDougal, D.H. and Gamlin, P.D. (2015) Autonomic control of the eye. Comprehensive Physiology 5(1): 439–473. DOI: https://doi.org/10.1002/cphy.c140014

Fotiou, D.F., Setergiou, V., Tsiptsios, D., Lithari, C., Nakou, M. and Karlovasitou, A. (2009) Cholinergic deficiency in Alzheimer’s and Parkinson’s disease: Evaluation with pupillometry. International Journal of Psychophysiology 73: 143–149. DOI: https://doi.org/10.1016/j.ijpsycho.2009.01.011

Nakayama, M., Nowak, W., Ishikawa, H., Asakawa, K. and Ichibe, Y. (2014) Discovering irregular pupil light responses to chromatic stimuli using waveform shapes of pupillograms. EURASIP J. in Bioinformatics and System Biology #18: 1–14. DOI: https://doi.org/10.1186/s13637-014-0018-x

Asanad, S., Ross-Cisneros, F.N., Barron, E., Nassisi, M., Sultan, W., Karanjia, R. and Sadun, A.A. (2019) The retinal choroid as an oculavascular biomarker for Alzheimer’s dementia: A histopathological study in severe disease. Alzheimer’s & Dimentia: Diagnosis, Assessment & Diesease Monitoring 11: 775–783. DOI: https://doi.org/10.1016/j.dadm.2019.08.005

Nowak, W., Nakayama, M., Kręcicki, T. and Hachoł, A.(2020) 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 6: 1–11. E6. DOI: https://doi.org/10.4108/eai.17-12-2020.167656

Gamlin, P.D., McDougal, D.H. and Pokorny, J. (2007) Human and macaque pupil responses driven by melanopisn-containing retinal ganglion cells. Vision Research 47: 946–954. DOI: https://doi.org/10.1016/j.visres.2006.12.015

Kawasaki, A. and Kardon, R.H. (2007) Intrinsically photosensitive retinal ganglion cells. Journal of Neuro-Ophthalmology 27: 195–204. DOI: https://doi.org/10.1097/WNO.0b013e31814b1df9

Sivak, J.M. (2013) The aging eye: Common degenerative mechanisms between the Alzheimer’s brain and retinal disease. Investigative Ophthalmology & Visual Science 54(1): 871–880. DOI: https://doi.org/10.1167/iovs.12-10827

Zivcevska, M., Blakeman, A., Lei, S., Goltz, H.C. and Wong, A.M.F. (2018) Binocular summation in postillumination pupil response driven by melanopsin-containing retinal ganglion cells. Visual Neuroscience 59: 4968–4977. DOI: https://doi.org/10.1167/iovs.18-24639

Sørensen, H., Goldsmith, J. and Sangalli, L.M. (2013) An introduction with medical applications to functional data analysis. Statistics in Medicine 32: 5222–5240. DOI: https://doi.org/10.1002/sim.5989

Pinkowski, B. (1994) Robust fourier descriptions for characterizing amplitude-modulated waveform shapes. Journal of Acoustical Society of America 95(6): 3419–3423. DOI: https://doi.org/10.1121/1.409961

Zhang, D. and Lu, G. (2002) A comparative study on shape retrieval using fourier descriptors with different shape signatures. In Proceedings of the 5th Asian Conference on Computer Vision (Springer): 646–651.

Nowak, W., Nakayama, M., Kręcicki, T., Trypka, E., Andrzejak, A. and Hachoł, A. (2019) Analysis for xtracted features of pupil light reflex to chromatic stim-uli in Alzheimer’s patients. EAI Endorsed Transactions on Pervasive Health and Technology 5: 1–10. E4. DOI: https://doi.org/10.4108/eai.13-7-2018.161750

Levitin, D.J., Nuzzo, R.L., Vines, B.W. and Ramsay, J.O. (2007) Introduction to functional data analysis. Candadian Psychology 48(3): 135–155. DOI: https://doi.org/10.1037/cp2007014

Ramsay, J., Hooker, G. and Graves, S. (2009) Functional Data Analysis with R and MATLAB (Springer, New York, USA). DOI: https://doi.org/10.1007/978-0-387-98185-7

Friedman, J., Hastie, T., Tibshirani, R. and Narasimhan, B. (2023), Package ’glmnet’: Lasso and elastic-net regu-larized generalized liner models. URL https://cloud.r-project.org/web/packages/glmnet/glmnet.pdf.

Nowak, W., Nakayama, M., Trypka, E. and Zarowska, A. (2021) Classification of Alzheimer’s disease patients using metric of oculo-motors. In Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS): 403–407. DOI: https://doi.org/10.15439/2021F32

Nowak, W., Z˛arowska, A., Szul-Pietrzak, E. and Misiuk-Hojło, M. (2014) System and measurement method for binocular pupillometry to study pupil size variability. BioMedical Engineering Online 13(#69): 1–16. DOI: https://doi.org/10.1186/1475-925X-13-69

López, O.A.M., López, A.M. and Crossa, J. (2022) Multivariate Statistical Machine Learning Methods for Genomic Prediction (Springer, New York, USA).

Downloads

Published

18-07-2024

How to Cite

[1]
M. Nakayama, W. Nowak, and T. Krecicki, “Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli”, EAI Endorsed Trans Int Sys Mach Lear App, vol. 1, Jul. 2024.