This monograph focuses on the development of diagnostic procedures for diseases related to retinal nerve conditions using features of waveforms of pupillary light reflex (PLR). Eye pupil reflection of light pulses is a well-known phenomenon, but the response mechanism is complicated. Some relationships of the features of chromatic light stimuli and activity of retinal ganglion cells influence the responses. As the features of pupil responses may represent the condition of nerve connections, these waveform shapes can detect symptoms of disease. The assistance of medical clinicians who have…mehr
This monograph focuses on the development of diagnostic procedures for diseases related to retinal nerve conditions using features of waveforms of pupillary light reflex (PLR). Eye pupil reflection of light pulses is a well-known phenomenon, but the response mechanism is complicated. Some relationships of the features of chromatic light stimuli and activity of retinal ganglion cells influence the responses. As the features of pupil responses may represent the condition of nerve connections, these waveform shapes can detect symptoms of disease. The assistance of medical clinicians who have shared PLR data from patients of aged-macular degeneration (AMD) or patients with impaired cognitive function, as caused by Alzheimer’s disease, for example, is gratefully acknowledged. The recorded temporal data of PLR waveforms were analyzed using multivariate analysis or data science techniques. As most participants were elderly or the number of measurements was insufficient, the signal processing required compensatory techniques. As a result, trials and re-analyses were carried out to better understand certain features. Although the best solutions have not yet been developed, the authors have decided to share the currently available procedures in this monograph and to solicit constructive suggestions for further improvement and development.
Dr. Minoru Nakayama is a Professor Emeritus of the Institute of Science Tokyo, Japan, and his current affiliation (since 2025) is Meiji University in Tokyo, Japan. He completed the Master of Education program at Tokyo Gakugei University in 1985 and was awarded a Doctor of Engineering degree from the Tokyo Institute of Technology in 1990. Dr. Nakayama previously held positions within the Department of Information and Communications Engineering and CRADLE (the Centre for Research & Development of Educational Technology) at the Tokyo Institute of Technology, which was renamed the Institute of Science Tokyo in 2024. His scientific research interests include human visual perception, human-computer interaction (HCI), and educational technology. He has international research experience, having been a Visiting Researcher at the University of Sussex, UK, and LIRMM at the University of Montpellier, France. He holds memberships in various academic organizations, including the ACM, BERA, IEICE, and JPA. Dr. Wioletta Nowak is an Assistant Professor at Department of Biomedical Engineering in Wroclaw University of Science and Technology. She completed the Master of Technical Physics program at Wroclaw University of Science and Technology in 2000 and was awarded a Doctor of Physics degree from the Wroclaw University of Science and Technology in 2005. Her scientific research interests include electromedical equipment construction, pupillometry, vision electrophysiology and cognititive study. She has international research experience, having worked at the Tokyo Institute of Technology.
Inhaltsangabe
Extracting irregular pupil light responses to chromatic stimuli using waveform shapes of pupillograms.- Prediction of Patients with Alzheimer’s Disease using Extracted Waveform Features of the Pupil Light Reflex in response to Chromatic Stimuli.- Classification of Participants using Metrics of Oculo-Motors during Dementia Tests.- Detection of Dementia Symptoms in Elderly using Features of Binocular Pupil Light Reflexes to Chromatic Light Pulses on Either Eye.- Detecting Procedures for Dementia Symptoms using Features in Differential Waveforms of the Pupil Light Reflex to Chromatic Stimuli.- Conclusions.
Extracting irregular pupil light responses to chromatic stimuli using waveform shapes of pupillograms.- Prediction of Patients with Alzheimer’s Disease using Extracted Waveform Features of the Pupil Light Reflex in response to Chromatic Stimuli.- Classification of Participants using Metrics of Oculo-Motors during Dementia Tests.- Detection of Dementia Symptoms in Elderly using Features of Binocular Pupil Light Reflexes to Chromatic Light Pulses on Either Eye.- Detecting Procedures for Dementia Symptoms using Features in Differential Waveforms of the Pupil Light Reflex to Chromatic Stimuli.- Conclusions.
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