Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory
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...
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Frontiers Media S.A.
2021-01-01
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Series: | Frontiers in Medicine |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2020.613708/full |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cheng Chen Li Zhan Xiaoxin Pan Zhiliang Wang Xiaoyu Guo Handai Qin Fen Xiong Wei Shi Min Shi Fei Ji Qiuju Wang Ning Yu Ruoxiu Xiao Ruoxiu Xiao |
spellingShingle |
Cheng Chen Li Zhan Xiaoxin Pan Zhiliang Wang Xiaoyu Guo Handai Qin Fen Xiong Wei Shi Min Shi Fei Ji Qiuju Wang Ning Yu Ruoxiu Xiao Ruoxiu Xiao Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory Frontiers in Medicine auditory brainstem response characteristic waveform recognition neural network model bi-directional long short-term memory wavelet transform |
author_facet |
Cheng Chen Li Zhan Xiaoxin Pan Zhiliang Wang Xiaoyu Guo Handai Qin Fen Xiong Wei Shi Min Shi Fei Ji Qiuju Wang Ning Yu Ruoxiu Xiao Ruoxiu Xiao |
author_sort |
Cheng Chen |
title |
Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory |
title_short |
Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory |
title_full |
Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory |
title_fullStr |
Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory |
title_full_unstemmed |
Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory |
title_sort |
automatic recognition of auditory brainstem response characteristic waveform based on bidirectional long short-term memory |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Medicine |
issn |
2296-858X |
publishDate |
2021-01-01 |
description |
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, human ABR was recorded. First, binarization is created to mark 1,024 sampling points accordingly. The selected characteristic area of ABR data is 0–8 ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bidirectional 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: The specific structure, related parameters, recognition effect, and noise resistance of the network were explored in 614 sets of ABR clinical data. The results show that the average detection time for each data was 0.05 s, and recognition accuracy reached 92.91%.Discussion: The study proposed an automatic recognition of ABR waveforms by using the BiLSTM-based machine learning technique. The results demonstrated that the proposed methods could reduce recording time and help doctors in making diagnosis, suggesting that the proposed method has the potential to be used in the clinic in the future. |
topic |
auditory brainstem response characteristic waveform recognition neural network model bi-directional long short-term memory wavelet transform |
url |
https://www.frontiersin.org/articles/10.3389/fmed.2020.613708/full |
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doaj-6060b703ea94499ca7e8210b436d1e542021-01-11T04:29:18ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-01-01710.3389/fmed.2020.613708613708Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term MemoryCheng Chen0Li Zhan1Xiaoxin Pan2Zhiliang Wang3Xiaoyu Guo4Handai Qin5Fen Xiong6Wei Shi7Min Shi8Fei Ji9Qiuju Wang10Ning Yu11Ruoxiu Xiao12Ruoxiu Xiao13School of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaCollege of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Otolaryngologic Diseases, Key Lab of Hearing Science, Ministry of Education, Beijing Key Lab of Hearing Impairment for Prevention and Treatment, Chinese PLA General Hospital, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, ChinaInstitute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, ChinaBackground: 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, human ABR was recorded. First, binarization is created to mark 1,024 sampling points accordingly. The selected characteristic area of ABR data is 0–8 ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bidirectional 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: The specific structure, related parameters, recognition effect, and noise resistance of the network were explored in 614 sets of ABR clinical data. The results show that the average detection time for each data was 0.05 s, and recognition accuracy reached 92.91%.Discussion: The study proposed an automatic recognition of ABR waveforms by using the BiLSTM-based machine learning technique. The results demonstrated that the proposed methods could reduce recording time and help doctors in making diagnosis, suggesting that the proposed method has the potential to be used in the clinic in the future.https://www.frontiersin.org/articles/10.3389/fmed.2020.613708/fullauditory brainstem responsecharacteristic waveform recognitionneural network modelbi-directional long short-term memorywavelet transform |