In order to improve the accuracy of a procedure for detecting symptoms of dementia, features of ocular oscillation are examined, in addition to features of waveforms of pupil light reflexes (PLRs) of both eyes in response to blue or red light pulses directed toward either eye. An experiment was conducted using elderly people with three levels of cognitive performance: Alzheimer's disease, mild cognitive impairment, and a normal control group. In order to classify participants, two regression functions using extracted variables were introduced to the diagnosis. The results confirm the effectiveness of measuring features of ocular oscillation as a method of classifying patients. As PLR responses of each eye were different during light pulse irradiations of the eye on the left or right side, overall performance may be improved by using two independent classifications.

A procedure for calculating the probability of levels of dementia is proposed using features of binocular pupil light reflex (PLR) to a chromatic light pulse on either eye. The PLR waveforms of 101 elderly participants, consisting of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) or belonging to a normal control group (NC), were measured during four experimental conditions. Three factor scores were calculated from the PLR waveform features for each response. Responses were summarised and the differences between the two eyes were calculated in order to detect asynchronicity of PLRs in response to a light pulse on either eye. Pupillary oscillation was also measured separately without light pulses, and frequency powers were evaluated. Probabilities for the level of dementia of each participant were predicted using two types of regression functions for MCI+AD or AD patients, which were optimised using a variable selection procedure for extracted features. In the results, some variables which represent asynchronous measurement and pupillary oscillation were selected, and the requirement of binocular measurement was confirmed. A prediction procedure for levels of dementia of participants using the optimised functions was proposed, and performance was evaluated.

A procedure for detecting cognitive impairment in senior citizens is examined using pupil light reflex (PLR) for chromatic light pulse and a portable measuring system.Features of PLRs of blue and red light pulses are compared.PLRs of elderly subjects were studied in order to develop a procedure for detection of the symptoms of cognitive function impairment using a dementia evaluation test.PLRs of both eyes were measured using blue and red light pulses aimed at either of the two eyes.The features of PLR waveforms for each eye were remained in comparable level for every group of participant.Three factor scores were calculated from the features, and a classification procedure for determining the level of dementia in a subject was created using regression analysis.As a result, the contribution of factor scores for blue light pulses according to a participant's age was confirmed.

Ocular information was observed during a set of dementia tests involving participants with Alzheimer's Disease (AD), with a mild level of cognitive impairment (MCI), or in a control group.The number of participants was 26.Features of changes in pupil size and in the central position of both eyes of participants of all three types were compared.There are significant differences in some of the metrics between the types, in earlier test sessions.The possibility of classification was confirmed using the extracted features, and the contributions of some features were examined.

A diagnostic procedure to predict the probability of diagnosing a patient with Alzheimer's Disease (AD) was developed using features of pupil light reflex (PLR) waveforms.15 features of PLRs for three colours of light pulses at two levels of brightness were measured.Participants were 12 AD patients and 7 control group subjects.A logistic regression analysis was introduced to identify AD patients using two factor scores of features of PLR.The prediction performance of combinations of factor scores for features of PLRs were then evaluated using a test of fitness.An MCMC technique was introduced to estimate the parameters of the regression functions.The model provides a distribution of the probability of diagnosis of AD patients and control group subjects.