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.

BackgroundThe waveforms of the pupillary light reflex (PLR) can be analyzed in a diagnostic test that allows for differentiation between disorders affecting photoreceptors and disorders affecting retinal ganglion cells, using various signal processing techniques. This procedure has been used on both healthy subjects and patients with age-related macular degeneration (AMD), as a simple diagnostic procedure is required for diagnosis.ResultsThe Fourier descriptor technique is used to extract the features of PLR waveform shapes of pupillograms and their amplitudes. To detect those patients affected by AMD using the extracted features, multidimensional scaling (MDS) and clustering techniques were used to emphasize stimuli and subject differences. The detection performance of AMD using the features and the MDS technique shows only a qualitative tendency, however. To evaluate the detection performance quantitatively, a set of combined features was created to evaluate characteristics of the PLR waveform shapes in detail. Classification performance was compared across three categories (AMD patients, aged, and healthy subjects) using the Random Forest method, and weighted values were optimized using variations of the classification error rates. The results show that the error rates for healthy pupils and AMD-affected pupils were low when the value of the coefficient for a combination of PLR amplitudes and features of waveforms was optimized as 1.5. However, the error rates for patients with age-affected eyes was not low.ConclusionsA classification procedure for AMD patients has been developed using the features of PLR waveform shapes and their amplitudes. The results show that the error rates for healthy PLRs and AMD PLRs were low when the Random Forest method was used to produce the classification. The classification of pupils of patients with age-affected eyes should be carefully considered in order to produce optimum results.Electronic supplementary materialThe online version of this article (doi:10.1186/s13637-014-0018-x) contains supplementary material, which is available to authorized users.

BackgroundAn objective and noninvasive examination of pupil size variability can be used to assess the activity of the autonomous nervous system. We designed a system that enables binocular, fast, and accurate recordings of different types of pupil variabilities, which are synchronous with other biosignals. This type of measurement system is needed to extend the scope of pupillometry applications.MethodsIn the proposed system, the left and right eyes are independently and interchangeably illuminated to generate alternating images, which are successively acquired by a single camera. The system is composed of four functional modules: the image acquisition module, the image processing unit, the light stimulator, and the controller. The proposed image processing algorithm approximates the shape of the pupil using the best-fit ellipse. The user control panel (controller) precisely sets the stimuli parameters and controls the entire measurement procedure.ResultsThe computer-based binocular system records the pupil size during the pupil light reflexes (direct and indirect) and spontaneous pupil size fluctuations, at a sampling rate up to 75 Hz, with a resolution better than 0.02 mm. Our initial laboratory tests confirmed that the new system is fast and precise (system accuracy better than 0.5% and repeatability better than 4%).ConclusionsThe proposed system’s unique geometry and construction, and the method it uses to detect images from each eye, allows us to monitor the right and left eyes using a single camera with no overlap between the images. The system does not require a very experienced operator, because it is relatively simple and easy to use. Importantly, it is comfortable for the subjects. Additionally, the presented system can operate with other bio-measurement systems using a synchronous signal. These system capabilities can expand the scope of pupillometry research applications.

No abstract

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.