Purpose: Electroretinography (ERG) is used to assess retinal function in ophthalmology clinics and animal models of ocular disease; however, analyzing ERG waveforms can be a time-intensive process with interobserver variability. We developed ERGAssist, an automated approach, to perform non-subjective and repeatable feature identification ("marking") of the ERG waveform.
Methods: The automated approach denoised the recorded waveforms and then located the b-wave after applying a lowpass filter. If an a-wave was present, the lowpass filter wave was also used to help locate the a-wave, which was considered the initial large negative response after the flash stimuli. Oscillatory potentials (OPs) were found using a bandpass filter on the denoised waveform. We used two cohorts. One was a Coherence cohort that consisted of ERGs with eight dark-adapted and three light-adapted stimuli in Brown Norway rats (-6 to 1.5 log cd·s/m2). The Verification cohort consisted of control and diabetic (DM) Long Evans rats. We examined retinal function using a five-step dark-adapted protocol (-3 to 1.9 log cd·s/m2).
Results: ERGAssist showed a strong correlation with manual markings of ERG features in our Coherence dataset, including the amplitudes (a-wave: r2 = 0.99; b-wave: r2 = 0.99; OP: r2 = 0.92) and implicit times (a-wave: r2 = 0.96; b-wave: r2 = 0.90; OP: r2 = 0.96). In the Verification cohort, both approaches detected differences between control and DM animals and found longer OP implicit times (P < 0.0001) in DM animals.
Conclusions: These results provide verification of ERGAssist to identify features of the full-field ERG.
Translational relevance: This ERG analysis approach can increase the rigor of basic science studies designed to investigate retinal function using full-field ERG. To aid the community, we have developed an open-source graphical user interface (GUI) implementing the methods presented.