Classification and Recognition of Noise-Induced Hearing Loss Based on P300 Event-Related Potential and LSTM-Attention Network
Pages 204 - 209
Abstract
In addressing the tendency for "pseudo-deafness" in the clinical diagnosis of noise-induced hearing loss (NIHL), this study proposes a method for identifying NIHL based on LSTM-Attention networks and P300 event-related potentials. During the data preprocessing stage, MNE is employed for the preprocessing of electroencephalogram signals, and Independent Component Analysis (ICA) is utilized to eliminate irrelevant noise from the data. In the identification and classification phase, leveraging the foundation of LSTM networks, an Attention mechanism is introduced to construct the LSTM-Attention classification and recognition network. The classification results for NIHL are generated using the sigmoid function. Experimental outcomes indicate that the proposed method effectively distinguishes differences in P300 event-related potentials between individuals with NIHL and those without, achieving an accuracy rate and mean Dice coefficient of 91.9% and 91.7%, respectively. These findings underscore the high accuracy and generalization performance of the proposed approach.
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Index Terms
- Classification and Recognition of Noise-Induced Hearing Loss Based on P300 Event-Related Potential and LSTM-Attention Network
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Published In
May 2024
279 pages
ISBN:9798400717666
DOI:10.1145/3674658
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Published: 18 November 2024
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ICBBT 2024
ICBBT 2024: 2024 16th International Conference on Bioinformatics and Biomedical Technology
May 24 - 26, 2024
Chongqing, China
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