ERG signal analysis using wavelet transform

Abstract

The wavelet analysis is a powerful tool for analyzing and detecting features of signals characterized by time-dependent statistical properties, as biomedical signals. The identification and the analysis of the components of these signals in the time-frequency domain, give meaningful information about the physiological mechanisms that govern them. This article presents the results of the wavelet analysis applied to the a-wave component of the human electroretinogram. In order to deepen and improve our knowledge about the behavior of the early photoreceptoral response, including the possible activation of interactions and correlations among the photoreceptors, we have detected and identified the stable time-frequency components of the a-wave, using six representative values of luminance. The results indicate the occurrence of three frequencies lying in the range 20-200 Hz. The lowest one is attributed to the summed activities of the photoreceptors. The others are weaker and at low luminance one of them does not occur. We relate them to the response of the rods and the cones whose aggregate activities are non-linear and typically exhibit self-organization under selective stimuli. The identification of the stable frequency components and of their times of occurrence helps us to shine light about the complex mechanisms governing the a-wave. The present results are promising toward the assessment of more refined model concerning the photoreceptoral activities.

A comparison among different techniques for human ERG signals processing and classification

“…Differently from other mother wavelets (Haar, Morlet, Daubechies), whose shapes are not similar to our traces, the MHW gives results in good agreement with the preliminary FA and for normal subjects has provided a good detection and localization of the characteristic features of the signal in regions of stationary activity that correspond either to minima or to maxima [38,39]. There exist different methods of displaying the results: we, here, calculate the absolute value of WT(s, s) coefficients and then normalize it to its maximum value for each trace [45e47]: WT abs ðs; sÞ ¼ absðWTðs; sÞÞ max½absðWTðs; sÞÞ 3The relevant frequencies are identified through the existence of clusters in the corresponding colour scalograms, that show the correlations among MHW and signal.…”

“…Each scalogram yields a dominant maximum (white cluster) f 0 and one or two local maxima, f 1 and f 2 . The analysis of the clusters in the scalograms shows that it is possible to individuate in normal subjects three stable frequency components, related to the activity of the photoreceptors, whereas the values and/or the number of these frequencies are indicative of an eventual pathology [38,39,49].…”

“…The lower frequency f 0 is associated with the combined activities of the photoreceptors, in the sense that its value takes into account the temporal distribution of the amount of excited photoreceptors [38,39]. The higher frequencies f 1 and f 2 are related to the contributions of the photoreceptors (f 1 to rods, f 2 to cones) and to their peculiarities [27].…”

Functional decomposition of the human ERG based on the discrete wavelet transform

“…This study also showed that the energy level, the frequency and the latency of the two OP frequency bands had distinct luminanceresponse (LR) functions, suggesting that they might be evoked by different retinal elements/mechanisms. The CWT also revealed that the scotopic a-wave of normal human subjects was composed of three frequency components (20,140,and 180 Hz) and that the higher frequency component was absent in achromates, suggesting that the CWT could also help in the diagnosis of photoreceptoral diseases (Barraco, Persano Adorno, & Brai, 2011). Moreover, DWT coefficients were shown to be superior to traditional TD measures in segregating normal and pathological PERG waveforms using principal components analysis (Rogala & Brykalski, 2005).…”

Chaotic Analysis of the Electroretinographic Signal for Diagnosis

“…A series of work followed by Barraco et al shows the three-frequency range of occurrence between 20 and 200 Hz [1] and also analyzed the time-frequency characteristics of the a -wave in congenital stationary night blindness (CSNB) patients [2, 6]. Study on the basis of principal component analysis and wavelet analysis was used to visualise the time domain features and wavelet features (Rogala and Brykalski) [7].…”