Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Intelligence in Medicine
Auditory brainstem response classification: A hybrid model using time and frequency features
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules
Engineering Applications of Artificial Intelligence
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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.