Performance Evaluation of Deep Learning Techniques on Classifying Commercial Sensor-based Auditory Brainstem Response-Reference-Cited by-同舟云学术

Performance Evaluation of Deep Learning Techniques on Classifying Commercial Sensor-based Auditory Brainstem Response

Published:2023-12-17 Issue: Volume:13 Page:1-4
ISSN:
Container-title:2023 16th International Conference on Sensing Technology (ICST)
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Author:

Huang Kaiyang¹,Wang Xin²,He Yuchao²,Ji Junyu²,Xu Yangjie³,Jia Desheng⁴,Zhao Chunrui⁴,Huang Jinzhu⁵,Tao Yuan⁶,Pan Hongguang⁴,Li Guanglin²,Chen Shixiong²

Affiliation:

1. Guangzhou University of Chinese Medicine,School of Medical Information Engineering,Guangzhou,China

2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen,China

3. Reliability and Trust University of Luxembourg,Interdisciplinary Centre for Security,Luxembourg,Luxembourg

4. Shenzhen Children’s Hospital,Department of Otolaryngology,Shenzhen,China

5. The First Dongguan Affiliated Hospital of Guangdong Medical University,Department of Endocrine,Dongguan,China

6. Peking University Shenzhen Hospital,Department of Otorhinolaryngology,Shenzhen,China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10460767/10460768/10460837.pdf?arnumber=10460837

Reference19 articles.

1. The characterization of auditory brainstem response (ABR) waveforms: A study in tree shrews (Tupaia belangeri)

2. Brainstem response (ABR) to rarefaction and condensation clicks in normal hearing and steep high-frequency hearing loss;Borg;Scandinavian audiology. Supplementum,1981

3. Auditory physics. Physical principles in hearing theory. III

4. Effects of click polarity on ABR peak latency and morphology in a clinical population;Pijl;The Journal of otolaryngology,1987