(untitled) (ELEKTRIKA- Journal of Electrical Engineering, 2024)
Hearing disorder is the most widespread sensory disability worldwide, impairing human communication and learning. An early and accurate hearing disorder detection system using an electroencephalogram (EEG) is the appropriate technique for dealing with this concern. The most significant modality for diagnosing hearing deficiency among EEG control signals is the auditory evoked potential (AEP), which is generated in the cortical region of the brain through auditory stimulus. This study aims to develop an efficient approach for detecting hearing disorders. For this purpose, this study has designed a hybrid model based on the convolutional operation of CNN and the SVM classifier. Initially, the CWT method was utilized to transform the raw AEP signals into time-frequency images. Then, the extracted features were classified using the proposed CSVM model. To test the robustness of the proposed model, this study also implemented a convolutional neural network (CNN) and support vector machine (SVM) with the same parameters. The experimental results with the hybrid CSVM model showed superior performance on the publicly available AEP dataset by achieving 94.48% testing accuracy, 96.40% precision, 92.96% recall, 94.65% F1 score, 88.95% Cohen Kappa score, which indicates that the proposed hybrid model could be used for early hearing disorder detection. Future enhancements will concentrate on identifying different hearing-signal-based data and the cloud-based, automated classification of AEP signals.