Retinal Electrophysiological Patterns in Alzheimer's Disease: A Multi-Domain Signal Processing Framework for Non-Invasive Biomarker Discovery Using a Portable ERG Device

Alzheimer's disease (AD) is a neurodegenerative disorder affecting more than 55 million people worldwide, with a diagnosis that remains predominantly clinical and frequently delayed. The electroretinogram (ERG) offers a non-invasive electrophysiological method for detecting retinal dysfunction associated with neurodegeneration; however, it remains unclear whether robust and reliable candidate biomarkers can be extracted from ERG signals beyond conventional amplitude- and latency-based parameters. Here we present a pilot study of a multi-domain signal processing framework applied to ERGs recorded from 46 participants (20 AD patients, 26 controls) with a handheld device (RETeval, LKC Technologies) using sinusoidal (1-50 Hz) and photopic ISCEV protocols. Five complementary techniques were implemented: (i) multiscale fuzzy entropy (MSFuzzyEn); (ii) FFT harmonic analysis; (iii) stimulus-response wavelet time-frequency coherence (WTC); (iv) a novel inter-cycle lag variant of sample entropy (