A Deep Learning Model for Wave V Peak Detection in Auditory Brainstem Response Data

In this study, we propose a YOLO-based object detection algorithm for the automated and accurate identification of the fifth wave (Wave V) in auditory brainstem response (ABR) graphs. The ABR test plays a critical role in the diagnosis of hearing disorders, with the fifth wave serving as a key marker for clinical assessment. However, conventional manual detection is time-consuming and subject to variability depending on the examiner’s expertise. To address these limitations, we developed a real-time detection method that utilizes a YOLO object detection model applied to ABR graph images. Prior to YOLO training, we employed a U-Net-based preprocessing algorithm to automatically remove existing annotated peaks from the ABR images, thereby generating training data suitable for peak detection. The proposed model was evaluated in terms of precision, recall, and mean average precision (mAP). The experimental results demonstrate that the YOLO-based approach achieves high detection performance across these metrics, indicating its potential as an effective tool for reliable Wave V peak localization in audiological applications.