Deep learning-based electroencephalic diagnosis of tinnitus symptom

Frontiers in Human Neuroscience

Neuroscience

Hyun-Seok Kim¹  Eul-Seok Hong²  Byoung-Kyong Min³  Sung Kwang Hong⁴  Dimitrios Pantazis⁵ 

[1] Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea;Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;Institute of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;Department of Otolaryngology, Hallym University College of Medicine, Anyang, Republic of Korea;McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States;Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States;

关键词: tinnitus;    electroencephalography;    diagnosis;    classification;    deep learning;   

DOI  :  10.3389/fnhum.2023.1126938

 received in 2022-12-18, accepted in 2023-04-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Tinnitus is a neuropathological phenomenon caused by the recognition of external sound that does not actually exist. Existing diagnostic methods for tinnitus are rather subjective and complicated medical examination procedures. The present study aimed to diagnose tinnitus using deep learning analysis of electroencephalographic (EEG) signals while patients performed auditory cognitive tasks. We found that, during an active oddball task, patients with tinnitus could be identified with an area under the curve of 0.886 through a deep learning model (EEGNet) using EEG signals. Furthermore, using broadband (0.5 to 50 Hz) EEG signals, an analysis of the EEGNet convolutional kernel feature maps revealed that alpha activity might play a crucial role in identifying patients with tinnitus. A subsequent time-frequency analysis of the EEG signals indicated that the tinnitus group had significantly reduced pre-stimulus alpha activity compared with the healthy group. These differences were observed in both the active and passive oddball tasks. Only the target stimuli during the active oddball task yielded significantly higher evoked theta activity in the healthy group compared with the tinnitus group. Our findings suggest that task-relevant EEG features can be considered as a neural signature of tinnitus symptoms and support the feasibility of EEG-based deep-learning approach for the diagnosis of tinnitus.

【 授权许可】

Unknown   
Copyright © 2023 Hong, Kim, Hong, Pantazis and Min.