The pupillary response, a reaction to light intensity, is well-known, and the pupillary light response controls the dilation and constriction of the pupil in response to changes in light intensity. The pupillary response has long been used for diagnostic procedures and psycho-physiological studies. The classification of pupils of patients with age-affected eyes should be carefully considered in order to produce optimum results. 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. ConclusionsĪ classification procedure for AMD patients has been developed using the features of PLR waveform shapes and their amplitudes. However, the error rates for patients with age-affected eyes was not low. 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. 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. To evaluate the detection performance quantitatively, a set of combined features was created to evaluate characteristics of the PLR waveform shapes in detail. ![]() The detection performance of AMD using the features and the MDS technique shows only a qualitative tendency, however. 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 Fourier descriptor technique is used to extract the features of PLR waveform shapes of pupillograms and their amplitudes. 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. The 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.
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