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.

Analysis of human eye pupil reactivity is a very valuable diagnostic method used mainly for evaluation of the condition of the autonomic nervous system and the visual system. The paper presents an experimental pupillometer built in the Institute of Physics of the Wroclaw University of Technology. The apparatus makes it possible to record and analyze pupillary light reflex and spontaneous changes in the pupil diameter during a session in the dark. The detector system used in the pupillometer allows us to record the pupil diameter at a rate of 90 Hz with a linear accuracy of 0.008 mm. In this paper, the proposed detection method, the principle of operation and calibration of the apparatus and the possibilities of the measurement system are presented.

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INTRODUCTION: Some Alzheimer's Disease (AD) patients respond to chromatic light stimulus, which may influence intrinsically photosensitive retinal ganglion cells (ipRGCs), due to factors common to both AD and Age-Related Macular Disease (AMD). OBJECTIVES: In this study, short light pulses of three colours were introduced to a novel diagnostic procesure for AD patients such as classification techniques using waveform features of pupil light reflexs (PLRs), and their prediction performances were evaluated. METHOD: PLRs to 1s pulses of red, blue and white stimuli shown at high and low photopic levels followed by a 7s restoration process were recorded after the stimuli were shown to 7 AD patients and 12 non-AD participants (aged 42-89). Features of waveform shapes of PLRs in 5 dimensions and 15 features of PLRs were extracted. RESULTS: In a classification analysis, most non-AD participants were correctly identified using the same level of performance we reported when PLRs for red and blue stimuli were used to measure the performance of AD patients. There were significant differences in some of the features of PLRs extracted from the two groups (AD and non-AD participants), particularly with the features for blue light stimuli in high brightness, which produced significant reactions in AD patients. The classification performance of using 15 features of the response to blue light stimuli was the highest among responses for all three colours, and was higher than the performance using the procedure in the previous study. Also, a few of the features extracted using the three colours of stimuli changed significantly across age ranges (70 and under, 71-80, and over 80), so these may indicate factors related to ageing. CONCLUSION: These results confirm that some specific features of PLRs, in particular the response to blue light, can indicate the existence of AD in patients. Also, a few of the features may reveal factors related to ageing during evaluations which use PLRs. This evidence may help in the better understanding of features of PLRs.

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