Detection procedures for Patients of Alzheimer’s Disease using Waveform Features of Pupil Light Reflex in response to Chromatic Stimuli

Abstract

INTRODUCTION: Various studies conducted to predict Alzheimer’s disease (AD) indicate that some pupillight reflex (PLR) features may contain symptoms of the disease. An effective procedure that can predict thedisease using PLRs is needed.OBJECTIVES: Two analytic approaches were examined in order to estimate the possibility of identifying Alzheimer’s patients using features of PLR waveforms from chromatic stimuli. In particular, an index of the probability of being an AD patient is introduced, and the features which contributed to PLRs the most were extracted.METHOD: PLRs for three colours of light pulses (red: 635nm, blue: 470nm, white: CIE x=0.28, y=0.31) at twolevels of intensity (10 and 100 cd/m2) were observed at 60Hz for 10s. Pulses consisted of pre-stimulus (2s), light pulse (1s) and restoration phases (7s). 15 features were extracted from each PLR waveform, such as pupil constriction velocity, pupil response delay, etc. Seven AD patients (age:42-84, mean=68.1) and 12 similar-aged control subjects (age:62-89, mean=72.1).RESULTS: The first approach was based on factor scores of features of PLRs. Two factor scores were extracted from the 15 features across all measurement conditions, and logistic functions were introduced in order to calculate the probability of identifying AD patients. Function parameters were estimated using a Bayesian technique, such as the Markov chain Monte Carlo method (MCMC). In consideration of the number of participants and biased data distributions, the second approach was based on the sparse modelling technique. Least absolute shrinkage and selection operator (LASSO) was applied to sets of PLR features from each light stimulus, together with the ages of subjects, and optimised result sets were obtained. Prediction performance was higher than with the previous procedure.CONCLUSION: The use of PLRs features from chromatic stimuli for identifying AD was developed and evaluated.

Detecting Symptoms of Dementia in Elderly Persons using Features of Pupil Light Reflex

“…The factor scores for blue but not red light pulses were selected once more for use in a regression model, following a stepwise procedure. The dominance of blue light pulses for the prediction was confirmed in a previous study [11], [12]. The possible reason for this may be based on the first hypothesis, which could not be confirmed according to the above evaluation, however.…”

“…Some typical features are observed such as deviation during the restoration process after the constriction. Several features of waveforms were extracted in order to compare groups of participants in relation to the previous study [12] as shown in Table I. The first hypothesis is that there is a feature difference between the left and right eyes when light pulses are directed at either eye.…”

“…Since every feature includes measurement errors and individual differences, the latent factors are extracted using factor analysis according to the method used in the previous study [12].…”

“…The factors of stimulus light wavelength and the age of patient were significant and are the major components of the deviation. In order to examine the effectiveness of the extracted features for a prediction of cognitive function impairment, an estimation procedure using a logistic analysis which had been introduced in a previous study [12] was conducted. Here, both MCI and AD patients are merged as the "AD+MCI" group since the number of MCI participants is limited.…”

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

“…Changes in the level of cognitive activity and mental workload handling ability are often assessed using pupillary changes [2], [3], [4]. The symptoms of AD and aged macular disease (AMD) are also detected using pupillary reactions to the pupil light reflex (PLR) [5], [6], [7].…”