Chaotic Analysis of the Electroretinographic Signal for Diagnosis

Abstract

Electroretinogram (ERG) is a time-varying potential which arises from different layers of retina. To be specific, all the physiological signals may contain some useful information which is not visible to our naked eye. However this subtle information is difficult to monitor directly. Therefore the ERG signal features which are extracted and analyzed using computers are highly useful for diagnosis. This work discusses the chaotic aspect of the ERG signal for the controls, congenital stationary night blindness (CSNB), and cone-rod dystrophy (CRD) classes. In this work, nonlinear parameters like Hurst exponent (HE), the largest Lyapunov exponent (LLE), Higuchi's fractal dimension (HFD), and approximate entropy (ApEn) are analyzed for the three different classes. It is found that the measures like HE dimension and ApEn are higher for controls as compared to the other two classes. But LLE shows no distinguishable variation for the three cases. We have also analyzed the recurrence plots and phase-space plots which shows a drastic variation among the three groups. The results obtained show that the ERG signal is highly complex for the control groups and less complex for the abnormal classes with P value less than 0.05.

Feature Extraction Methods for Electroretinogram Signal Analysis: A Review

“…Although linear methods are mostly used in analyzing ERG signals, nonlinear features also show aspects that cannot be investigated using linear methods. However, published works in this field are still deficient [109][110][111][112][113]. Using nonlinear tools to represent chaotic behaviors can be a good choice for discovering the ERG signal's nature.…”

“…Molaie et al [113] worked on flash frequency and contrast as effective parameters in the ERG recordings. They reported that these parameters significantly impact the recorded signals and lead to bifurcations, indicating chaotic behavior.…”

Electroretinogram Analysis In Patients With Non-Proliferative Diabetic Retinopathy

“…These features include the Lyapunov exponent, Hurst exponent, approximate entropy, and fractal dimension. Nonlinear features have made a good distinction between healthy and abnormal groups [27]. Another feature that could show the dynamic of ERG in CRVO patients was mapping the ERG to a parabolic curve and extracting an angle criterion based on the arrangement of points on a new curve [28].…”

“…Different signal processing methods have been applied to ERG, including timedomain, frequency-domain, and time-frequency domain analyses [20][21][22][23][24][25][26]. Besides, nonlinear features have been used to analyze the ERG [27][28].…”

Comparison among Short Time Fourier Transform and Continuous Wavelet Transform Techniques in Flash Erg Signals Processing

“…Rod yanıtı karanlık adaptasyon sonrasında fERG ölçümlerinde elde edilen ilk sinyaldir. Kon yanıtı aydınlık adaptasyon sonrası kon hücrelerinden, maksimum kombine yanıtı ise karanlık adaptasyon sonrası hem rod hem de kon hücrelerinden elde edilen yanıtlardır [12].…”