Matching Pursuit Algorithm Applied to the Evaluation of Click-evoked Otoacoustic Emissions Recorded with Linear and Nonlinear Protocols

Typically, click-evoked otoacoustic emissions (CEOAEs) are analyzed using fast Fourier transform which has greatly limited time-frequency (t-f) resolution. The purpose of the present study was to process CEOAEs recorded using linear and non-linear presentation modes by applying the Matching Pursuit (MP) method of adaptive approximation which provides a high t-f resolution. The data were recorded in 26 normal-hearing subjects using the ILO96 system with clicks presented at 78-82 dB pSPL. In the non-linear mode, a series of four clicks was delivered with three at the same level and polarity and the fourth three times greater in amplitude and inverted in polarity, whereas in the linear mode all stimuli were presented at the same level and polarity. The responses consisting of 512 data points were stored in two buffers. For each subject, 520 responses were recorded separately for off-line MP analyses. The MP method allowed decomposition of signals into waveforms of defined frequency, latency, time span, and amplitude and also identified patterns of resonance modes that were characteristic for CEOAEs recorded in each individual ear. The overall CEOAE levels were higher by 4 dB for the linear mode than those for the nonlinear method, in agreement with studies reported previously. In general, t-f properties of CEOAEs recorded with linear and non-linear protocols were similar with the exception of the 0-6-ms post-stimulus time window and the frequency range below 2.2 kHz. This part of the signal was contaminated by a stimulus artifact in the linear mode. The reproducibility factor grew faster with an increase of the number of averaged responses for the linear protocol than for the non-linear method. The results suggest that main differences between CEOAEs measured with the two methods are related to stimulus artifacts occurring at the beginning of the recording time-window in the linear mode