A Simple Algorithm for Objective Threshold Determination of Auditory Brainstem Responses

The auditory brainstem response (ABR) is a sound-evoked neural response commonly used to assess auditory function in humans and laboratory animals. ABR thresholds are typically chosen by visual inspection, leaving the procedure susceptible to user bias. We sought to develop an algorithm to automate determination of ABR thresholds to eliminate such biases and to standardize approaches across investigators and laboratories. Two datasets of mouse ABR waveforms obtained from previously published studies of normal ears as well as ears with varying degrees of cochlear-based threshold elevations (Maison et al., 2013; Sergeyenko et al., 2013) were reanalyzed using an algorithm based on normalized cross-covariation of adjacent level presentations. Correlation-coefficient vs. level data for each ABR level series were fit with both a sigmoidal and two-term power function. From these fits, threshold was interpolated at different criterion values of correlation-coefficient ranging from 0 to 0.5. The criterion value of 0.35 was selected by comparing visual thresholds to computed thresholds across all frequencies tested. With such a criterion, the mean algorithm-computed thresholds were comparable to the visual thresholds noted by two independent observers for each data set. The success of the algorithm was also qualitatively assessed by comparing averaged waveforms at the thresholds determined by the two methods, and quantitatively assessed by comparing peak 1 amplitude growth functions expressed as dB re each of the two threshold measures. Application of a cross-covariance analysis to ABR waveforms can emulate visual thresholding decisions made by highly trained observers. Unlike previous applications of similar methodologies using template matching, our algorithm performs only intrinsic comparisons within ABR sets, and therefore is more robust to equipment and investigator differences in assessing waveforms, as evidenced by similar results across the two datasets.