Speech Auditory Brainstem Response Signal Processing: Estimation, Modeling, Detection, and Enhancement

Abstract

The speech auditory brainstem response (sABR) is a promising technique for assessing the function of the auditory system. This non-invasive technique has shown utility as a marker of central processing disorders, some types of learning difficulties in children, and potentially for fitting hearing aids. However, the sABR needs a long recording time to obtain a reliable signal due to the high background noise, which limits its clinical applicability. The objective of this work is to develop methods to detect the sABR in high background noise and enhance it based on a modeling approach and through experimental testing. First, sABR noise estimation based on LQ/QR decomposition is derived, and its mathematical proof is shown. Second, an autoregression model is used to estimate the single-trial sABR which is then used to test several sABR detection and enhancement methods. Third, a novel Artificial Neural Network (ANN) based detection approach is proposed and compared using modeled and recorded data to other detection methods in the literature: Optimal Linear Filter (LF), Online Estimator (OE), Mutual Information (MI) and Artificial Neural Network based on the Discrete Wavelet Transform and Approximate Entropy (ANN DA). Finally, comprehensive evaluation of several sABR enhancement methods is performed, based on the Wiener Filter (WF), Maximum-SNR Filter (Max-SNR), Adaptive Noise Cancellation (ANC) with Least-Mean-Square (LMS), Affine Projection (AP) and Recursive-Least-Square (RLS) adaptation algorithm. The results show that the developed LQ/QR decomposition estimated noise is similar to the actual noise, and the modeled data are statistically similar to the recorded data. Moreover, the proposed ANN-based detection method is more accurate and requires less processing time than other methods, and the comprehensive evaluation of enhancement methods shows that RLS has best overall performance in enhancing the sABR. Therefore, the methods developed and evaluated in this work have the potential to reduce the required recording time for the sABR, and thus make it more practical as a clinical tool.