Obtaining reliable phase-gradient delays from otoacoustic emission data

Abstract

Reflection-source otoacoustic emission phase-gradient delays are widely used to obtain noninvasive estimates of cochlear function and properties, such as the sharpness of mechanical tuning and its variation along the length of the cochlear partition. Although different data-processing strategies are known to yield different delay estimates and trends, their relative reliability has not been established. This paper uses in silico experiments to evaluate six methods for extracting delay trends from reflection-source otoacoustic emissions (OAEs). The six methods include both previously published procedures (e.g., phase smoothing, energy-weighting, data exclusion based on signal-to-noise ratio) and novel strategies (e.g., peak-picking, all-pass factorization). Although some of the methods perform well (e.g., peak-picking), others introduce substantial bias (e.g., phase smoothing) and are not recommended. In addition, since standing waves caused by multiple internal reflection can complicate the interpretation and compromise the application of OAE delays, this paper develops and evaluates two promising signal-processing strategies, the first based on time-frequency filtering using the continuous wavelet transform and the second on cepstral analysis, for separating the direct emission from its subsequent reflections. Altogether, the results help to resolve previous disagreements about the frequency dependence of human OAE delays and the sharpness of cochlear tuning while providing useful analysis methods for future studies.

Distortion-product otoacoustic emission reflection-component delays and cochlear tuning: Estimates from across the human lifespan

“…In part, the increase at low frequencies also reflects the relative paucity of data in this region, where the SNR is generally worse and the effects of the data selection criteria more severe. As with the trends themselves, the CIs obtained using the two algorithms are similar, despite the difference in sampling, consistent with previous results on in silico subjects (Shera and Bergevin, 2012). Overall the CIs obtained using peak picking are somewhat smaller, despite being based on a much smaller fraction of the data.…”

“…The sizes of the corresponding 95% confidence intervals for the trend are plotted in the lower panel. We note a few principal observations: (1) The peak picking and energy weighting algorithms produce nearly identical trends in all groups, consistent with in silico results (Shera and Bergevin, 2012). The two methods do yield small systematic differences, with the trends from energy weighting generally falling slightly below those from peak picking.…”

“…Group trend lines for the N R delays were computed using locally linear regression, also known as loess smoothing (Cleveland, 1993;Shera and Bergevin, 2012). Both coordinates were log-transformed before finding the trend; log-transforming the ordinate is necessary to equalize the variance across frequency.…”

“…Loess smoothing was performed using two strategies found to be most effective in recent work: Energy weighting and peak picking (Shera and Bergevin, 2012). Both methods emphasize data at the peaks of the magnitude fine structure, although in different ways, and both were combined with SNR-based exclusion as described above.…”

“…2 Here, we apply measurements of the reflection-source component of the 2f 1 Àf 2 distortion product otoacoustic emission (DPOAE) to estimate OAE delays and cochlear tuning in humans. Our principal objectives are twofold: (1) to explore proposed signal processing and data analysis strategies for extracting reliable delay trends from OAE data (Shera and Bergevin, 2012) and (2) to generate preliminary estimates of reflection-source DPOAE delay and cochlear tuning for age groups representing seven decades of the human lifespan.…”