Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection

Abstract

This paper presents a novel system for automatic recognition of auditory brainstem responses (ABR) to detect hearing threshold. ABR is an important potential signal for determining objective audiograms. Its detection is usually performed by medical experts with often basic signal processing techniques. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients and a set of discrete wavelet transform (DWT) approximation coefficients are calculated and extracted from signals separately as three different sets of feature vectors. These features are then selected by a modified adaptive method, which mainly supports to the input dimension reduction via selecting the most significant feature components. In the second stage, the feature vectors are classified by a support vector machine (SVM) classifier which is a powerful advanced technique for solving supervised binary classification problem due to its generalization ability. After that the proposed system is applied to real ABR data and it is resulted in a very good sensitivity, specificity and accuracy levels for DCT coefficients such as 99.2%, 94.0% and 96.2%, respectively. Consequently, the proposed system can be used for recognition of ABRs for hearing threshold detection.