Comparative Analysis of Empirical Mode Decomposition and Discrete Wavelet Transform as Denoising Methods for Auditory Brainstem Response

Abstract

Peak latency measurement of the patient's Auditory Brainstem Response (ABR) essential wave components (Waves I-V) is the usual method in hearing screening to determine the likelihood of hearing impairment. To visualize the peaks of Waves I-V, averaging about 2000 ABR sweeps is necessary for reducing the background noise caused by power line interference and myogenic activity; however, this method is time-consuming and inconvenient for patients and healthcare workers. The study aims to use signal denoising methods to denoise ABRs averaged with fewer sweeps without affecting their functionality. Two deterministic signal denoising approaches, Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT), were evaluated and compared to determine which could produce functional denoised ABRs using fewer sweeps. For the 1 kHz stimulus frequency, DWT produced functional ABRs with fewer sweeps than EMD for stimulus intensities of 75, 65, 55 and 50 dB peSPL. For the 4 kHz stimulus frequency, only the DWT method could produce functional ABRs with fewer sweeps. DWT method performs better than EMD in producing clinically relevant denoised ABR for most stimulus descriptions. The findings can help audiologists use the DWT denoising approach when averaging noisy ABRs with fewer sweeps to address the problems caused by the time-consuming conventional averaging method.