Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory - Chen, Cheng; Zhan, Li; Pan, Xiaoxin; Wang, Zhiliang; Guo, Xiaoyu; Qin, Handai; Xiong, Fen; Shi, Wei; Shi, Min; Ji, Fei; Wang, Qiuju; Yu, Ning; et al. (Frontiers in Medicine, 2021)
Background: Auditory brainstem response (ABR) testing is an invasive electrophysiological auditory function test. Its waveforms and threshold can reflect auditory functional changes in the auditory centers in the brainstem and are widely used in the clinic to diagnose dysfunction in hearing. However, identifying its waveforms and threshold is mainly dependent on manual recognition by experimental persons, which could be primarily influenced by individual experiences. This is also a heavy job in clinical practice. Methods: In this work, the human ABR was recorded. First, binarization is created to mark 1024 sampling points accordingly. The selected characteristic area of ABR data is 0-8ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bi-directional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding. Results: Specific structure, related parameters, recognition effect, and noise resistance of network were explored in 614 sets of ABR clinical data. The results show that the average detection time for each data was 0.05s, and recognition accuracy reached 92.91%. Discussion: The study proposed an automatic recognition of ABR waveforms by using BiLSTM based machine learning technique. The results demonstrated that the proposed methods could reduce recording time and help the doctor to diagnose, suggesting that the proposed method has the potential to be used in the clinic in the future.