Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli
Keywords:
Pupil Light Reflex, Alzheimer’s Disease, functional data analysisAbstract
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.
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.
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.
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.
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.
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.
Kawasaki, A. and Kardon, R.H. (2007) Intrinsically photosensitive retinal ganglion cells. Journal of Neuro-Ophthalmology 27: 195–204.
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.
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.
Sørensen, H., Goldsmith, J. and Sangalli, L.M. (2013) An introduction with medical applications to functional data analysis. Statistics in Medicine 32: 5222–5240.
Pinkowski, B. (1994) Robust fourier descriptions for characterizing amplitude-modulated waveform shapes. Journal of Acoustical Society of America 95(6): 3419–3423.
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.
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.
Ramsay, J., Hooker, G. and Graves, S. (2009) Functional Data Analysis with R and MATLAB (Springer, New York, USA).
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.
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.
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
How to Cite
Issue
Section
License
Copyright (c) 2024 Minoru Nakayama, Wioletta Nowak, Tomasz krecicki
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
