Research Article
Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli
@ARTICLE{10.4108/eetismla.6070, author={Minoru Nakayama and Wioletta Nowak and Tomasz Krecicki}, title={Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli}, journal={EAI Endorsed Transactions on Intelligent Systems and Machine Learning}, volume={1}, number={1}, publisher={EAI}, journal_a={ISMLA}, year={2024}, month={7}, keywords={Pupil Light Reflex, Alzheimer’s Disease, functional data analysis}, doi={10.4108/eetismla.6070} }
- Minoru Nakayama
Wioletta Nowak
Tomasz Krecicki
Year: 2024
Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli
ISMLA
EAI
DOI: 10.4108/eetismla.6070
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.
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