Review of various feature extraction approaches for ERG signal analysis: Advantages and drawbacks

Abstract

This article presents a comprehensive examination of various techniques used to extract features from Electroretinogram (ERG) signals for analysis purposes. ERG signals are crucial in the diagnosis and study of retinal diseases. The accurate extraction of informative features from ERG signals is vital for understanding retinal function and identifying abnormalities. This review specifically focuses on different methods employed for feature extraction in ERG signal analysis, highlighting their respective advantages and disadvantages. The article explores a range of established methods, namely time-domain, frequency-domain, time-frequency domain analysis, and machine learning delves into the difficulties and constraints linked to these strategies, such as signal noise, artifacts, and computational complexity. Its objective is to offer a thorough evaluation of the merits and drawbacks of diverse feature extraction techniques, with the aim of aiding researchers and clinicians in their selection of suitable methods for the analysis of ERG signals.