Healthy physiological states characteristically exhibit higher signal complexity than pathological states. Relying on the principle that the retina is a direct extension of the central nervous system, we use the eye as a “window to the brain.”
By applying sophisticated non-linear analyses—such as Multi-Scale Entropy (MSE) and other complexity measures—to electroretinogram (ERG) recordings, we seek to identify novel digital biomarkers. Our goal is to enable the early diagnosis and tracking of neurodegenerative disorders like Alzheimer’s disease. By looking beyond standard linear metrics, we aim to harness these “hidden” signal dynamics to create non-invasive, accessible, and highly accurate diagnostic tools for clinical settings.
Leo Medina
Principal Investigator
Leo teaches engineering courses at Usach, and his research interests are in the neural engineering and computational neuroscience fields. His work has contributed to understand how nerve fibers respond to electrical stimulation.