Objective classification of auditory brainstem responses to consonant-vowel syllables using local discriminant bases

Abstract

ObjectiveThe main goal of this study is classifying the auditory brainstem responses to consonant-vowels /da/, /ba/, and /ga/ into three classes automatically. MethodAuditory brainstem responses (ABRs) to consonant-vowel syllables /da/, /ba/, and /ga/ were recorded from twenty-seven normal subjects. Time domain features (i.e., energy, entropy, cosine distance and correlation coefficient) and frequency domain features (i.e., magnitude and phase of the frequency responses) were extracted. Also, local discriminant bases (LDB) method was employed to extract time-frequency features from wavelet packet coefficients. Then, random subset feature selection (RSFS) algorithm reduced the feature spaces. Afterward, discriminant analysis (DA), naive Bayes (NB), multiclass support vector machine (MSVM) and K-nearest neighbors (KNN) methods classified the selected features. ResultsThe time–frequency domain features showed better results than other features (91.36%). The maximum accuracy was achieved for MSVM classifier when we used a combination of all the features (97.5%). ConclusionThis study shows the efficiency of frequency and time–frequency domains features. The results indicate that time–frequency features obtained by local discriminant bases were more successful in objective classifications of the responses. Besides, the selected features from the phase of the frequency responses were reliable in classifying the responses to consonant-vowels /da/, /ba/ and /ga/. SignificanceThe importance of this study lies in the fact that it helps the objective classification of auditory brainstem responses underlying the different encoding of three consonant-vowels (/ba/, /da/ and /ga/) in frequency and time–frequency domains.